A benchmark dataset and workflow for landslide susceptibility zonation
Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map,...
Saved in:
| Published in | Earth-science reviews Vol. 258; p. 104927 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.11.2024
|
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data.
Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community.
With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods.
Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence.
We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences. |
|---|---|
| AbstractList | Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences. Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact of landslides worldwide. As such, it is the subject of countless scientific studies. Many methods exist for generating a susceptibility map, mostly falling under the definition of statistical or machine learning. These models try to solve a classification problem: given a collection of spatial variables, and their combination associated with landslide presence or absence, a model should be trained, tested to reproduce the target outcome, and eventually applied to unseen data. Contrary to many fields of science that use machine learning for specific tasks, no reference data exist to assess the performance of a given method for landslide susceptibility. Here, we propose a benchmark dataset consisting of 7360 slope units encompassing an area of about 4,100km2 in Central Italy. Using the dataset, we tried to answer two open questions in landslide research: (1) what effect does the human variability have in creating susceptibility models; (2) how can we develop a reproducible workflow for allowing meaningful model comparisons within the landslide susceptibility research community. With these questions in mind, we released a preliminary version of the dataset, along with a “call for collaboration,” aimed at collecting different calculations using the proposed data, and leaving the freedom of implementation to the respondents. Contributions were different in many respects, including classification methods, use of predictors, implementation of training/validation, and performance assessment. That feedback suggested refining the initial dataset, and constraining the implementation workflow. This resulted in a final benchmark dataset and landslide susceptibility maps obtained with many classification methods. Values of area under the receiver operating characteristic curve obtained with the final benchmark dataset were rather similar, as an effect of constraints on training, cross–validation, and use of data. Brier score results show larger variability, instead, ascribed to different model predictive abilities. Correlation plots show similarities between results of different methods applied by the same group, ascribed to a residual implementation dependence. We stress that the experiment did not intend to select the “best” method but only to establish a first benchmark dataset and workflow, that may be useful as a standard reference for calculations by other scholars. The experiment, to our knowledge, is the first of its kind for landslide susceptibility modeling. The data and workflow presented here comparatively assess the performance of independent methods for landslide susceptibility and we suggest the benchmark approach as a best practice for quantitative research in geosciences. |
| ArticleNumber | 104927 |
| Author | Rossi, Mauro Samodra, Guruh Camera, Corrado A.S. Schüßler, Nick Susyanto, Nanang Steger, Stefan Wahyudi, Erwin Eko Satyam, Neelima Woodard, Jacob B. Gazibara, Sanja Bernat Moreno, Mateo Scaringi, Gianvito Bajni, Greta Gupta, Kunal Grohmann, Carlos H. Rivera-Rivera, Jhonatan Loche, Marco Abraham, Minu Treesa Marchesini, Ivan Torizin, Jewgenij Jacobs, Liesbet Alvioli, Massimiliano Mirus, Benjamin B. Sinčić, Marko Sirbu, Flavius Bornaetxea, Txomin Lombardo, Luigi Aguilera, Héctor |
| Author_xml | – sequence: 1 givenname: Massimiliano surname: Alvioli fullname: Alvioli, Massimiliano email: massimiliano.alvioli@irpi.cnr.it organization: Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, I-06128 Perugia, Italy – sequence: 2 givenname: Marco surname: Loche fullname: Loche, Marco organization: Institute of Hydrogeology, Engineering Geology and Applied Geophysics, Charles University, Albertov 6, 128 43 Prague, Czech Republic – sequence: 3 givenname: Liesbet surname: Jacobs fullname: Jacobs, Liesbet organization: Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, the Netherlands – sequence: 4 givenname: Carlos H. surname: Grohmann fullname: Grohmann, Carlos H. organization: Institute of Astronomy, Geophysics and Atmospheric Sciences, Universidade de São Paulo, Rua do Matão 1226, 05508-090, São Paulo, SP, Brazil – sequence: 5 givenname: Minu Treesa surname: Abraham fullname: Abraham, Minu Treesa organization: Methods for Model–based Development in Computational Engineering, RWTH Aachen, 52062, Germany – sequence: 6 givenname: Kunal surname: Gupta fullname: Gupta, Kunal organization: Department of Civil Engineering, Indian Institute of Technology, Indore, India – sequence: 7 givenname: Neelima surname: Satyam fullname: Satyam, Neelima organization: Department of Civil Engineering, Indian Institute of Technology, Indore, India – sequence: 8 givenname: Gianvito surname: Scaringi fullname: Scaringi, Gianvito organization: Institute of Hydrogeology, Engineering Geology and Applied Geophysics, Charles University, Albertov 6, 128 43 Prague, Czech Republic – sequence: 9 givenname: Txomin surname: Bornaetxea fullname: Bornaetxea, Txomin organization: Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, I-06128 Perugia, Italy – sequence: 10 givenname: Mauro surname: Rossi fullname: Rossi, Mauro organization: Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, I-06128 Perugia, Italy – sequence: 11 givenname: Ivan surname: Marchesini fullname: Marchesini, Ivan organization: Consiglio Nazionale delle Ricerche, Istituto di Ricerca per la Protezione Idrogeologica, via Madonna Alta 126, I-06128 Perugia, Italy – sequence: 12 givenname: Luigi surname: Lombardo fullname: Lombardo, Luigi organization: Faculty of Geo–Information Science and Earth Observation (ITC), University of Twente, PO Box 217, Enschede AE 7500, the Netherlands – sequence: 13 givenname: Mateo surname: Moreno fullname: Moreno, Mateo organization: Faculty of Geo–Information Science and Earth Observation (ITC), University of Twente, PO Box 217, Enschede AE 7500, the Netherlands – sequence: 14 givenname: Stefan surname: Steger fullname: Steger, Stefan organization: GeoSphere Austria, Vienna, Austria – sequence: 15 givenname: Corrado A.S. surname: Camera fullname: Camera, Corrado A.S. organization: Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, Milan, Italy – sequence: 16 givenname: Greta surname: Bajni fullname: Bajni, Greta organization: Dipartimento di Scienze della Terra "A. Desio", Università degli Studi di Milano, Milan, Italy – sequence: 17 givenname: Guruh surname: Samodra fullname: Samodra, Guruh organization: Department of Environmental Geography, Faculty of Geography, Universitas Gadjah Mada, Indonesia – sequence: 18 givenname: Erwin Eko surname: Wahyudi fullname: Wahyudi, Erwin Eko organization: Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia – sequence: 19 givenname: Nanang surname: Susyanto fullname: Susyanto, Nanang organization: Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Indonesia – sequence: 20 givenname: Marko surname: Sinčić fullname: Sinčić, Marko organization: Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia – sequence: 21 givenname: Sanja Bernat surname: Gazibara fullname: Gazibara, Sanja Bernat organization: Department of Geology and Geological Engineering, Faculty of Mining, Geology and Petroleum Engineering, University of Zagreb, Pierottijeva 6, HR-10000 Zagreb, Croatia – sequence: 22 givenname: Flavius surname: Sirbu fullname: Sirbu, Flavius organization: Institute for Advance Environmental Research, West University of Timisoara, Oituz 4, 300086 Timişoara, Romania – sequence: 23 givenname: Jewgenij surname: Torizin fullname: Torizin, Jewgenij organization: Federal Institute for Geosciences and Natural Resources, Hannover, Germany – sequence: 24 givenname: Nick surname: Schüßler fullname: Schüßler, Nick organization: Federal Institute for Geosciences and Natural Resources, Hannover, Germany – sequence: 25 givenname: Benjamin B. surname: Mirus fullname: Mirus, Benjamin B. organization: U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO, United States of America – sequence: 26 givenname: Jacob B. surname: Woodard fullname: Woodard, Jacob B. organization: U.S. Geological Survey, Geologic Hazards Science Center, Golden, CO, United States of America – sequence: 27 givenname: Héctor surname: Aguilera fullname: Aguilera, Héctor organization: Geological Survey of Spain (IGME-CSIC), Rios Rosas 23, 28003 Madrid, Spain – sequence: 28 givenname: Jhonatan surname: Rivera-Rivera fullname: Rivera-Rivera, Jhonatan organization: Geological Survey of Spain (IGME-CSIC), Rios Rosas 23, 28003 Madrid, Spain |
| BookMark | eNqNkLtOAzEURF0EiQT4BlzSbLC9TxcUUUQAKRIN1JYfd4WTzTrYTqLw9TgsoqCB6uqOZkajM0Gj3vWA0DUlU0podbuagvRBWw_7KSOsSGrBWT1CY0IoyxpWsnM0CWFF0k94PUaLGVbQ67eN9GtsZJQBIpa9wQfn123nDrh1HndJCZ01gMMuaNhGq2xn4xF_uF5G6_pLdNbKLsDV971Ar4v7l_ljtnx-eJrPlpnOeR0zw3LJc9KmUbpmkvOaK2C5aghREoqmqTU1VdkAmFxTRstSE9WWoIwxrGhUfoFuht6td-87CFFsbBrUpYHgdkHktCxoRZqiSta7waq9C8FDK7SNX2Ojl7YTlIgTM7ESP8zEiZkYmKV8_Su_9TZhOv4jORuSkEjsLXiRTAkymGTVURhn_-z4BKTlkUc |
| CitedBy_id | crossref_primary_10_1126_sciadv_adt1541 crossref_primary_10_1016_j_geomorph_2025_109728 crossref_primary_10_1016_j_jag_2025_104365 crossref_primary_10_1016_j_ecolind_2025_113313 crossref_primary_10_3390_geosciences14100280 crossref_primary_10_3390_rs17030381 crossref_primary_10_1016_j_envsoft_2024_106217 |
| Cites_doi | 10.1016/S0169-555X(99)00078-1 10.1093/bioinformatics/bti623 10.1007/s11069-023-06092-w 10.1016/j.cageo.2015.04.007 10.1016/j.ecolmodel.2019.108815 10.5194/nhess-14-259-2014 10.1016/0045-7949(91)90469-3 10.1016/j.ocemod.2021.101943 10.1016/j.geomorph.2020.107041 10.1023/A:1010933404324 10.1007/s00477-021-02165-z 10.1016/j.envsoft.2019.104565 10.5194/gmd-15-5651-2022 10.1007/s10064-020-01733-x 10.25080/Majora-92bf1922-00a 10.1007/s00254-007-0882-8 10.5194/nhess-24-823-2024 10.1016/j.scitotenv.2023.165289 10.1080/00036840110058482 10.1007/s10346-023-02091-x 10.5194/essd-14-4129-2022 10.1371/journal.pone.0169748 10.1007/s00477-020-01893-y 10.1016/j.geomorph.2006.04.007 10.1038/s41598-021-00780-y 10.1016/j.ecolmodel.2013.08.011 10.1016/j.enggeo.2010.09.009 10.1007/s10346-011-0283-7 10.1016/j.scitotenv.2021.145935 10.1016/j.enggeo.2005.02.002 10.1007/s42452-020-3060-1 10.1016/j.jsg.2016.03.005 10.1016/j.catena.2018.03.003 10.1038/s41598-023-28991-5 10.1016/j.enggeo.2022.106586 10.1002/gj.4666 10.5194/gmd-9-3533-2016 10.18637/jss.v063.i19 10.3390/rs12030346 10.1016/j.catena.2018.12.035 10.1016/j.jsg.2016.03.003 10.1016/S0098-3004(97)00117-9 10.1016/j.ecolmodel.2019.06.002 10.4113/jom.2009.1041 10.1016/j.scitotenv.2021.147360 10.1016/j.cageo.2023.105364 10.1016/j.catena.2020.104580 10.1007/s11069-022-05554-x 10.5194/nhess-18-2455-2018 10.1016/j.jsames.2024.104805 10.1016/j.geomorph.2022.108401 10.5194/nhess-6-687-2006 10.1186/1471-2105-12-77 10.1007/s10346-013-0391-7 10.3390/geosciences11100425 10.1016/j.geomorph.2017.12.007 10.1007/s11629-021-7254-9 10.1016/j.earscirev.2022.104125 10.1016/j.enggeo.2019.105237 10.1145/1007730.1007735 10.1017/CBO9780511802843 10.1016/j.geomorph.2016.03.015 10.1016/S0013-7952(03)00143-1 10.1016/j.geomorph.2017.10.018 10.1016/S0304-3800(02)00193-X 10.1016/j.catena.2020.105067 10.1016/j.geomorph.2020.107084 10.1080/17538947.2016.1169561 10.1016/j.gsf.2023.101645 10.1038/s41586-020-2649-2 10.1016/S0013-7952(00)00041-7 10.1007/s11069-023-06103-w 10.1016/j.patrec.2005.10.010 10.1007/s11004-023-10105-6 10.1016/j.earscirev.2012.02.001 10.5194/nhess-13-3339-2013 10.1016/j.geomorph.2010.09.004 10.1016/j.geomorph.2009.10.002 10.5194/nhess-14-95-2014 10.1007/s10346-014-0550-5 10.1080/10106049.2018.1559885 10.1016/j.geomorph.2020.107124 10.1023/B:NHAZ.0000007172.62651.2b 10.1007/s10346-010-0213-0 10.1007/s11069-023-06153-0 10.1016/j.cageo.2017.03.022 10.1016/j.geomorph.2021.107804 10.1007/s00477-022-02215-0 10.1007/s10064-024-03730-w 10.1016/j.catena.2020.104630 10.1002/bjs.10895 10.1080/10106049.2022.2087753 10.1016/j.gsf.2023.101765 10.1002/esp.3290160505 10.1007/s00477-021-02020-1 10.1007/s10064-023-03163-x 10.1038/323533a0 10.1016/j.earscirev.2020.103225 10.5194/gmd-9-3975-2016 10.1186/s40677-020-00152-0 10.1111/j.1469-1809.1936.tb02137.x 10.1016/j.scitotenv.2023.169166 10.1016/j.geomorph.2011.03.001 10.1016/j.earscirev.2018.03.001 10.1016/j.enggeo.2018.07.019 10.1007/s12517-015-2150-7 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 10.1016/j.geomorph.2004.09.025 10.1016/j.enggeo.2011.09.006 |
| ContentType | Journal Article |
| Copyright | 2024 The Author(s) |
| Copyright_xml | – notice: 2024 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION 7S9 L.6 |
| DOI | 10.1016/j.earscirev.2024.104927 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geology |
| ExternalDocumentID | 10_1016_j_earscirev_2024_104927 S0012825224002551 |
| GeographicLocations | Italy |
| GeographicLocations_xml | – name: Italy |
| GroupedDBID | --K --M -DZ -~X .~1 0R~ 186 1B1 1RT 1~. 1~5 29G 4.4 457 4G. 5GY 5VS 6I. 6TJ 7-5 71M 8P~ 9JN 9M8 AACTN AAEDT AAEDW AAFTH AAHBH AAIKC AAIKJ AAKOC AALRI AAMNW AAOAW AAQFI AAQXK AAXKI AAXUO ABFNM ABJNI ABMAC ABQEM ABQYD ABWVN ABXDB ACDAQ ACGFO ACGFS ACGOD ACIWK ACLVX ACRLP ACRPL ACSBN ADBBV ADEZE ADMUD ADNMO AEBSH AEIPS AEKER AENEX AFFNX AFJKZ AFTJW AGHFR AGNAY AGUBO AGYEJ AHHHB AI. AIEXJ AIKHN AITUG AKRWK ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ASPBG ATOGT AVWKF AXJTR AZFZN BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EJD EO8 EO9 EP2 EP3 FA8 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HMA HVGLF HZ~ IHE IMUCA J1W KOM LY3 M41 MO0 MVM N9A O-L O9- OAUVE OHT OZT P-8 P-9 P2P PC. PQQKQ PZZ Q38 R2- RIG ROL RPZ RXW SCC SDF SDG SDP SEP SES SEW SPC SPCBC SSE SSZ T5K TAE TN5 UQL VH1 WH7 WUQ XJT ZCA ZKB ZMT ~02 ~G- AATTM AAYWO AAYXX ACVFH ADCNI ADXHL AEUPX AFPUW AFXIZ AGCQF AGQPQ AGRNS AIGII AIIUN AKBMS AKYEP APXCP BNPGV CITATION EFKBS 7S9 L.6 SSH |
| ID | FETCH-LOGICAL-c397t-d23a930f492c72a9979be23b800bae4887c1d658eed3c12155c0bf5ebddd248b3 |
| IEDL.DBID | .~1 |
| ISSN | 0012-8252 |
| IngestDate | Wed Jul 02 04:37:15 EDT 2025 Thu Apr 24 23:03:47 EDT 2025 Tue Aug 05 11:59:07 EDT 2025 Sat Mar 01 15:46:30 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Benchmark dataset Statistical modeling Landslide inventory Landslide susceptibility mapping Machine learning Spatial analysis Geomorphometry Landslide susceptibility Slope units Geomorphological mapping |
| Language | English |
| License | This is an open access article under the CC BY license. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c397t-d23a930f492c72a9979be23b800bae4887c1d658eed3c12155c0bf5ebddd248b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://www.sciencedirect.com/science/article/pii/S0012825224002551 |
| PQID | 3154160846 |
| PQPubID | 24069 |
| ParticipantIDs | proquest_miscellaneous_3154160846 crossref_citationtrail_10_1016_j_earscirev_2024_104927 crossref_primary_10_1016_j_earscirev_2024_104927 elsevier_sciencedirect_doi_10_1016_j_earscirev_2024_104927 |
| PublicationCentury | 2000 |
| PublicationDate | November 2024 2024-11-00 20241101 |
| PublicationDateYYYYMMDD | 2024-11-01 |
| PublicationDate_xml | – month: 11 year: 2024 text: November 2024 |
| PublicationDecade | 2020 |
| PublicationTitle | Earth-science reviews |
| PublicationYear | 2024 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Loche, Alvioli, Marchesini, Bakka, Lombardo (bb0480) 2022; 232 Erener, Sivas, Selcuk-Kestel, Düzgün (bb0250) 2017; 104 Wei, Simko (bb0765) 2021 Fang, Wang, van Westen, Lombardo (bb0265) 2024; 126 Rolain, Alvioli, Nguyen, Nguyen, Jacobs, Kervyn (bb0615) 2023; 118 Mela, Kopalle (bb0530) 2002; 34 Yong, Jinlong, Guo, Bin, Tao, Hao, Li, Qinghua (bb0795) 2022; 36 Camera, Bajni, Corno, Raffa, Stevenazzi, Apuani (bb0165) 2021; 786 Lombardo, Tanyas (bb0495) 2022; 36 Openshaw (bb0560) 1984 Schlögel, Marchesini, Alvioli, Reichenbach, Rossi, Malet (bb0660) 2018; 301 Loche, Alvioli, Marchesini, Lombardo (bb0485) 2023 Guzzetti, Reichenbach, Ardizzone, Cardinali, Galli (bb0305) 2006; 81 GitHub (bb0280) 2022 Aguilera, Lombardo, Tanyas, Lipani (bb0010) 2022; 36 Amato, Eisank, Castro-Camilo, Lombardo (bb0050) 2019; 260 Kirby, Grilli, Horrillo, Liu, Nicolsky, Abadie, Ataie-Ashtiani, Castro, Clous, Escalante, Fine, González-Vida, Løvholt, Lynett, Ma, Macías, Ortega, Shi, Yavari-Ramshe, Zhang (bb0410) 2022; 170 Kingma, Ba (bb0405) 2017 Mirus, Woodard (bb0545) 2023 Bordoni, Galanti, Bartelletti, Persichillo, Barsanti, Giannecchini, D’Amato Avanzi, Cevasco, Brandolini, Galve, Meisina (bb0095) 2020; 193 Wang, Cheng, Marconcini, Bachofer, Liu, Xiong, Lombardo (bb0755) 2022; 301 Petschko, Brenning, Bell, Goetz, Glade (bb0570) 2014; 14 Buiter, Schreurs, Albertz, Gerya, Kaus, Landry, le Pourhiet, Mishin, Egholm, Cooke, Maillot, Thieulot, Crook, May, Souloumiac, Beaumont (bb0155) 2016; 92 Lee, Sameen, Pradhan, Park (bb0435) 2018; 303 Alvioli, Guzzetti, Marchesini (bb0045) 2020; 358 Amato, Fiorucci, Martino, Lombardo, Palombi (bb0055) 2023; 82 Budimir, Atkinson, Lewis (bb0150) 2015; 12 Goetz, Guthrie, Brenning (bb0285) 2011; 129 Tien Bui, Thai Pham, Quoc Nguyen, Hoang (bb0735) 2016; 9 McKinney (bb0525) 2010 Liu, Zhang, Li, Huang, Wang (bb0470) 2022; 37 Shcheglovitova, Anderson (bb0680) 2013; 269 Jia, Alvioli, Gariano, Marchesini, Guzzetti, Tang (bb0380) 2021; 389 Akgun (bb0020) 2012; 9 Camera, Bajni (bb0160) 2023 Ivakhnenko, Lapa (bb0365) 1967; vol. 8 Scaringi, Loche (bb0650) 2023 Jacobs, Kervyn, Reichenbach, Rossi, Marchesini, Alvioli, Dewitte (bb0370) 2020; 356 Dahal, Lombardo (bb0205) 2023; 176 Dias, Grohmann (bb0235) 2024; 135 Di Napoli, Tanyas, Castro-Camilo, Calcaterra, Cevasco, Di Martire, Pepe, Brandolini, Lombardo (bb0230) 2023; 119 Merghadi, Yunus, Dou, Whiteley, ThaiPham, Bui, Avtar, Abderrahmane (bb0535) 2020; 207 Ermini, Catani, Casagli (bb0255) 2005; 66 Alvioli, Marchesini, Reichenbach, Rossi, Ardizzone, Fiorucci, Guzzetti (bb0040) 2016; 9 Joliffe (bb0385) 2002 Chung, Fabbri (bb0200) 2003; 30 Sahin (bb0635) 2020; 2 Jordahl, Van den Bossche, Fleischmann, Wasserman, McBride, Gerard, Tratner, Perry, Garcia Badaracco, Farmer, Hjelle, Snow, Cochran, Gillies, Culbertson, Bartos, Eubank, Maxalbert, Bilogur, Rey, Ren, Arribas-Bel, Wasser, Wolf, Journois, Wilson, Greenhall, Holdgraf, Filipe, Leblanc (bb0390) 2020 Ranstam, Cook (bb0595) 2018; 105 Rumelhart, Hinton, Williams (bb0630) 1986; 323 Rabby, Li, Hilafu (bb0590) 2023; 13 Huang, Xiong, Jiang, Yao, Fan, Catani, Chang, Zhou, Huang, Liu (bb0355) 2024; 104700 Gong (bb0295) 2006 Aguilera, Rivera Rivera, Guardiola-Albert, Béjar-Pizarro (bb0015) 2023 Fawcett (bb0270) 2006; 27 Schreurs, Buiter, Boutelier, Burberry, Callot, Cavozzi, Cerca, Chen, Cristallini, Cruden, Cruz, Daniel, Da Poian, Garcia, Gomes, Grall, Guillot, Guzmán, Hidayah, Hilley, Klinkmüller, Koyi, Lu, Maillot, Meriaux, Nilfouroushan, Pan, Pillot, Portillo, Rosenau, Schellart, Schlische, Take, Vendeville, Vergnaud, Vettori, Wang, Withjack, Yagupsky, Yamada (bb0670) 2016; 92 Breiman (bb0125) 2011; 45 Dahal, Tanyas, van Westen, van der Meijde, Mai, Huser, Lombardo (bb0210) 2024; 24 Das, Sarkar, Kanungo (bb0215) 2022; 115 Süzen, Doyuran (bb0725) 2004; 71 Chung, Fabbri (bb0195) 1999; 65 Wang, Dahal, Fang, van Westen, Yin, Lombardo (bb0760) 2024; 15 Beigaitė, Mechenich, Žliobaitė (bb0080) 2022 Leoni, Barchiesi, Catallo, Dramis, Fubelli, Lucifora, Mattei, Pezzo, Puglisi (bb0440) 2009; 5 Rossi, Bornaetxea, Reichenbach (bb0625) 2022; 15 Satyam, Abraham, Gupta (bb0645) 2023 Brabb, Pampeyan, Bonilla (bb0115) 1972 Liang, Wang, Khan (bb0450) 2021; 35 Zeng, Wu, Peduto, Glade, Hayakawa, Yin (bb0800) 2023; 14 Bornaetxea, Rossi, Marchesini, Alvioli (bb0100) 2018; 18 Akgun, Dag, Bulut (bb0025) 2008; 54 Pokharel, Alvioli, Lim (bb0575) 2021; 11 Mărgărint, Grozavu, Patriche (bb0515) 2013; 13 Wood (bb0775) 2017 Thai Pham, Prakash, Dou, Singh, Phan Trong, Trung Tran, Minh Le, Van Phong, Dang Kim, Shirzadi, Tien Bui (bb0730) 2020; 35 Regmi, Giardino, Vitek (bb0600) 2010; 115 Liaw, Wiener (bb0455) 2002; 2 Kuhn, Wickham (bb0420) 2020 Luzi, Pergalani, Terlien (bb0510) 2000; 58 Shano, Raghuvanshi, Meten (bb0675) 2020; 7 Torizin, Schüßler (bb0740) 2023 Samodra, Wahyudi, Susyanto (bb0640) 2023 Hong, Miao, Liu, Zhu (bb0335) 2019; 176 Marjanović, Kovačević, Bajat, Voženílek (bb0520) 2011; 123 Reichenbach, Rossi, Malamud, Mihir, Guzzetti (bb0605) 2018; 180 Steger, Mair, Kofler, Pittore, Zebisch, Schneiderbauer (bb0715) 2021; 776 R Core Team (bb0585) Carrara, Cardinali, Detti, Guzzetti, Pasqui, Reichenbach (bb0170) 1991; 16 Ho (bb0330) 1995 Lindgren, Rue (bb0465) 2015; 63 Brier (bb0140) 1950; 78 UCI (bb0750) 2024 Steger, Schmaltz, Glade (bb0710) 2020; 354 Bajni, Camera, Brenning, Apuani (bb0065) 2022; 415 Hengl, de Jesus, Heuvelink, Gonzalez, Kilibarda, Blagotić, Shangguan, Wright, Geng, Bauer-Marschallinger (bb0325) 2017; 12 Allen (bb0035) 1997 Sin Yin, Othman, Chong Khoo (bb0685) 2010 Bornaetxea, Yazdani, Rossi (bb0110) 2023 Bragagnolo, da Silva, Grzybowski (bb0120) 2020; 123 Lima, Steger, Glade, Murillo-García (bb0460) 2022; 19 Huang, Zhao (bb0345) 2018; 165 Kassambara, Mundt (bb0395) 2020 Robin, Turck, Hainard, Tiberti, Lisacek, Sanchez, Müller (bb0610) 2011; 12 Yeon, Han, Ryu (bb0785) 2010; 116 Trigila, Iadanza, Spizzichino (bb0745) 2010; 7 Lucchese, de Oliveira, Pedrollo (bb0500) 2021; 198 Harris, Millman, van der Walt, Gommers, Virtanen, Cournapeau, Wieser, Taylor, Berg, Smith, Kern, Picus, Hoyer, van Kerkwijk, Brett, Haldane, del Río, Wiebe, Peterson, Gérard-Marchant, Sheppard, Reddy, Weckesser, Abbasi, Gohlke, Oliphant (bb0315) 2020; 585 Leung, Wong, Kwan, Petley (bb0445) 2024; 83 Liu, Wang, Zhang, He, Pijush (bb0475) 2023; 58 Davison, Hinkley (bb0225) 1997 Bajni, Camera, Apuani (bb0070) 2023 Atkinson, Massari (bb0060) 1998; 24 Rossi, Reichenbach (bb0620) 2016; 9 Lee, Evangelista (bb0430) 2006; 6 Ali (bb0030) 2020 Batista, Prati, Monard (bb0075) 2004; 6 Chen, Guestrin (bb0185) 2016 Sterlacchini, Ballabio, Blahut, Masetti, Sorichetta (bb0720) 2011; 125 Lee (bb0425) 2019; 35 Yesilnacar, Topal (bb0790) 2005; 79 Dias, Hölbling, Grohmann (bb0240) 2021; 11 Moreno, Steger (bb0550) 2023 Brenning (bb0130) 2008 Guzzetti, Carrara, Cardinali, Reichenbach (bb0300) 1999; 31 Wood, Augustin (bb0780) 2002; 157 Agterberg, Bonham-Carter, Wright (bb0005) 1990 Schloerke, Cook, Larmarange, Briatte, Marbach, Thoen, Elberg, Toomet, Crowley, Hofmann, Wickham (bb0655) 2022 Brenning (bb0135) 2012 Chalkias, Polykretis, Karymbalis, Soldati, Ghinoi, Ferentinou (bb0180) 2020; 79 Sinčić, Bernat Gazibara, Krkač, Lukačić, Mihalić Arbanas (bb0690) 2023 Hosmer, Lemeshow, Sturdivant (bb0340) 2013 Schratz, Muenchow, Iturritxa, Richter, Brenning (bb0665) 2019; 406 Fang, Wang, van Westen, Lombardo (bb0260) 2023; 56 Elia, Castellaro, Dahal, Lombardo (bb0245) 2023; 898 Kavzoglu, Sahin, Colkesen (bb0400) 2014; 11 Bucci, Santangelo, Fongo, Alvioli, Cardinali, Melelli, Marchesini (bb0145) 2022; 14 Goetz, Brenning, Petschko, Leopold (bb0290) 2015; 81 Lombardo, Mai (bb0490) 2018; 244 Zuur, Ieno, Walker, Saveliev, Smith (bb0805) 2009 Meyer, Reudenbach, Wöllauer, Nauss (bb0540) 2019; 411 Bivand, Keitt, Rowlingson, Pebesma, Sumner, Hijmans, Baston, Rouault, Warmerdam, Ooms, Rundel (bb0085) 2023 James, Witten, Hastie, Tibshirani (bb0375) 2013 Huang, Cao, Guo, Jiang, Li, Guo (bb0350) 2020; 191 Bornaetxea, Remondo, Bonachea, Valenzuela (bb0105) 2023; 118 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay (bb0565) 2011; 12 Prakash, Manconi, Loew (bb0580) 2020; 12 Bonham-Carter, Agterberg, Wright (bb0090) 1990 Chen, Chai, Sun, Wang, Ding, Hong (bb0190) 2016; 9 Moreno, Lombardo, Crespi, Zellner, Mair, Pittore, van Westen, Steger (bb0555) 2024; 912 Davis, Goadrich (bb0220) 2006 Kuhn, Silge (bb0415) 2022 ISRIC (bb0360) 2024 Sing, Sander, Beerenwinkel, Lengauer (bb0695) 2005; 21 Wislocki, Bentley (bb0770) 1991; 40 Fisher (bb0275) 1936; 7 Sirbu (bb0700) 2023 Luckman (bb0505) 1987 Carrara, Cardinali, Guzzetti, Reichenbach (bb0175) 1995 Heckmann, Gegg, Gegg, Becht (bb0320) 2014; 14 Guzzetti, Mondini, Cardinali, Fiorucci, Santangelo, Chang (bb0310) 2012; 112 Steger, Brenning, Bell, Petschko, Glade (bb0705) 2016; 262 Bivand (10.1016/j.earscirev.2024.104927_bb0085) Bordoni (10.1016/j.earscirev.2024.104927_bb0095) 2020; 193 Hosmer (10.1016/j.earscirev.2024.104927_bb0340) 2013 Chalkias (10.1016/j.earscirev.2024.104927_bb0180) 2020; 79 Robin (10.1016/j.earscirev.2024.104927_bb0610) 2011; 12 Jia (10.1016/j.earscirev.2024.104927_bb0380) 2021; 389 Bornaetxea (10.1016/j.earscirev.2024.104927_bb0110) 2023 Kuhn (10.1016/j.earscirev.2024.104927_bb0415) 2022 Torizin (10.1016/j.earscirev.2024.104927_bb0740) 2023 Bajni (10.1016/j.earscirev.2024.104927_bb0065) 2022; 415 Schloerke (10.1016/j.earscirev.2024.104927_bb0655) Wang (10.1016/j.earscirev.2024.104927_bb0755) 2022; 301 Wei (10.1016/j.earscirev.2024.104927_bb0765) McKinney (10.1016/j.earscirev.2024.104927_bb0525) 2010 Pedregosa (10.1016/j.earscirev.2024.104927_bb0565) 2011; 12 Shano (10.1016/j.earscirev.2024.104927_bb0675) 2020; 7 Yesilnacar (10.1016/j.earscirev.2024.104927_bb0790) 2005; 79 Erener (10.1016/j.earscirev.2024.104927_bb0250) 2017; 104 Goetz (10.1016/j.earscirev.2024.104927_bb0290) 2015; 81 Wood (10.1016/j.earscirev.2024.104927_bb0780) 2002; 157 Lindgren (10.1016/j.earscirev.2024.104927_bb0465) 2015; 63 Breiman (10.1016/j.earscirev.2024.104927_bb0125) 2011; 45 Liang (10.1016/j.earscirev.2024.104927_bb0450) 2021; 35 Prakash (10.1016/j.earscirev.2024.104927_bb0580) 2020; 12 Alvioli (10.1016/j.earscirev.2024.104927_bb0040) 2016; 9 Bornaetxea (10.1016/j.earscirev.2024.104927_bb0105) 2023; 118 Hengl (10.1016/j.earscirev.2024.104927_bb0325) 2017; 12 Tien Bui (10.1016/j.earscirev.2024.104927_bb0735) 2016; 9 Loche (10.1016/j.earscirev.2024.104927_bb0485) 2023 Yong (10.1016/j.earscirev.2024.104927_bb0795) 2022; 36 Bucci (10.1016/j.earscirev.2024.104927_bb0145) 2022; 14 R Core Team (10.1016/j.earscirev.2024.104927_bb0585) Ho (10.1016/j.earscirev.2024.104927_bb0330) 1995 Scaringi (10.1016/j.earscirev.2024.104927_bb0650) 2023 Ali (10.1016/j.earscirev.2024.104927_bb0030) 2020 Guzzetti (10.1016/j.earscirev.2024.104927_bb0300) 1999; 31 Lee (10.1016/j.earscirev.2024.104927_bb0425) 2019; 35 Joliffe (10.1016/j.earscirev.2024.104927_bb0385) 2002 Bragagnolo (10.1016/j.earscirev.2024.104927_bb0120) 2020; 123 Ermini (10.1016/j.earscirev.2024.104927_bb0255) 2005; 66 Dias (10.1016/j.earscirev.2024.104927_bb0240) 2021; 11 GitHub (10.1016/j.earscirev.2024.104927_bb0280) Regmi (10.1016/j.earscirev.2024.104927_bb0600) 2010; 115 Batista (10.1016/j.earscirev.2024.104927_bb0075) 2004; 6 Bonham-Carter (10.1016/j.earscirev.2024.104927_bb0090) 1990 Shcheglovitova (10.1016/j.earscirev.2024.104927_bb0680) 2013; 269 Lima (10.1016/j.earscirev.2024.104927_bb0460) 2022; 19 Dias (10.1016/j.earscirev.2024.104927_bb0235) 2024; 135 Satyam (10.1016/j.earscirev.2024.104927_bb0645) 2023 Kirby (10.1016/j.earscirev.2024.104927_bb0410) 2022; 170 Lee (10.1016/j.earscirev.2024.104927_bb0430) 2006; 6 Sinčić (10.1016/j.earscirev.2024.104927_bb0690) 2023 Trigila (10.1016/j.earscirev.2024.104927_bb0745) 2010; 7 Mirus (10.1016/j.earscirev.2024.104927_bb0545) 2023 Sahin (10.1016/j.earscirev.2024.104927_bb0635) 2020; 2 Steger (10.1016/j.earscirev.2024.104927_bb0715) 2021; 776 Fang (10.1016/j.earscirev.2024.104927_bb0260) 2023; 56 Fang (10.1016/j.earscirev.2024.104927_bb0265) 2024; 126 Liu (10.1016/j.earscirev.2024.104927_bb0470) 2022; 37 Moreno (10.1016/j.earscirev.2024.104927_bb0555) 2024; 912 Kingma (10.1016/j.earscirev.2024.104927_bb0405) 2017 Dahal (10.1016/j.earscirev.2024.104927_bb0210) 2024; 24 Liu (10.1016/j.earscirev.2024.104927_bb0475) 2023; 58 Budimir (10.1016/j.earscirev.2024.104927_bb0150) 2015; 12 Samodra (10.1016/j.earscirev.2024.104927_bb0640) 2023 Carrara (10.1016/j.earscirev.2024.104927_bb0170) 1991; 16 Davis (10.1016/j.earscirev.2024.104927_bb0220) 2006 Jacobs (10.1016/j.earscirev.2024.104927_bb0370) 2020; 356 Jordahl (10.1016/j.earscirev.2024.104927_bb0390) 2020 Loche (10.1016/j.earscirev.2024.104927_bb0480) 2022; 232 Ivakhnenko (10.1016/j.earscirev.2024.104927_bb0365) 1967; vol. 8 Liaw (10.1016/j.earscirev.2024.104927_bb0455) 2002; 2 Fawcett (10.1016/j.earscirev.2024.104927_bb0270) 2006; 27 Steger (10.1016/j.earscirev.2024.104927_bb0710) 2020; 354 Huang (10.1016/j.earscirev.2024.104927_bb0355) 2024; 104700 Alvioli (10.1016/j.earscirev.2024.104927_bb0045) 2020; 358 Ranstam (10.1016/j.earscirev.2024.104927_bb0595) 2018; 105 UCI (10.1016/j.earscirev.2024.104927_bb0750) 2024 Fisher (10.1016/j.earscirev.2024.104927_bb0275) 1936; 7 Schratz (10.1016/j.earscirev.2024.104927_bb0665) 2019; 406 Thai Pham (10.1016/j.earscirev.2024.104927_bb0730) 2020; 35 Harris (10.1016/j.earscirev.2024.104927_bb0315) 2020; 585 Hong (10.1016/j.earscirev.2024.104927_bb0335) 2019; 176 Petschko (10.1016/j.earscirev.2024.104927_bb0570) 2014; 14 Di Napoli (10.1016/j.earscirev.2024.104927_bb0230) 2023; 119 Sterlacchini (10.1016/j.earscirev.2024.104927_bb0720) 2011; 125 Zuur (10.1016/j.earscirev.2024.104927_bb0805) 2009 Chen (10.1016/j.earscirev.2024.104927_bb0190) 2016; 9 Reichenbach (10.1016/j.earscirev.2024.104927_bb0605) 2018; 180 Zeng (10.1016/j.earscirev.2024.104927_bb0800) 2023; 14 Lucchese (10.1016/j.earscirev.2024.104927_bb0500) 2021; 198 Merghadi (10.1016/j.earscirev.2024.104927_bb0535) 2020; 207 Rossi (10.1016/j.earscirev.2024.104927_bb0620) 2016; 9 Mela (10.1016/j.earscirev.2024.104927_bb0530) 2002; 34 Akgun (10.1016/j.earscirev.2024.104927_bb0020) 2012; 9 Chung (10.1016/j.earscirev.2024.104927_bb0200) 2003; 30 Steger (10.1016/j.earscirev.2024.104927_bb0705) 2016; 262 Luckman (10.1016/j.earscirev.2024.104927_bb0505) 1987 Amato (10.1016/j.earscirev.2024.104927_bb0050) 2019; 260 Brabb (10.1016/j.earscirev.2024.104927_bb0115) 1972 Elia (10.1016/j.earscirev.2024.104927_bb0245) 2023; 898 Meyer (10.1016/j.earscirev.2024.104927_bb0540) 2019; 411 Lombardo (10.1016/j.earscirev.2024.104927_bb0490) 2018; 244 Brier (10.1016/j.earscirev.2024.104927_bb0140) 1950; 78 Davison (10.1016/j.earscirev.2024.104927_bb0225) 1997 Pokharel (10.1016/j.earscirev.2024.104927_bb0575) 2021; 11 Beigaitė (10.1016/j.earscirev.2024.104927_bb0080) 2022 Huang (10.1016/j.earscirev.2024.104927_bb0345) 2018; 165 Gong (10.1016/j.earscirev.2024.104927_bb0295) 2006 Yeon (10.1016/j.earscirev.2024.104927_bb0785) 2010; 116 Agterberg (10.1016/j.earscirev.2024.104927_bb0005) 1990 Goetz (10.1016/j.earscirev.2024.104927_bb0285) 2011; 129 Guzzetti (10.1016/j.earscirev.2024.104927_bb0305) 2006; 81 Guzzetti (10.1016/j.earscirev.2024.104927_bb0310) 2012; 112 Camera (10.1016/j.earscirev.2024.104927_bb0165) 2021; 786 Aguilera (10.1016/j.earscirev.2024.104927_bb0015) 2023 Akgun (10.1016/j.earscirev.2024.104927_bb0025) 2008; 54 Chung (10.1016/j.earscirev.2024.104927_bb0195) 1999; 65 James (10.1016/j.earscirev.2024.104927_bb0375) 2013 Wood (10.1016/j.earscirev.2024.104927_bb0775) 2017 Sing (10.1016/j.earscirev.2024.104927_bb0695) 2005; 21 Schreurs (10.1016/j.earscirev.2024.104927_bb0670) 2016; 92 Mărgărint (10.1016/j.earscirev.2024.104927_bb0515) 2013; 13 Camera (10.1016/j.earscirev.2024.104927_bb0160) 2023 Kavzoglu (10.1016/j.earscirev.2024.104927_bb0400) 2014; 11 Leoni (10.1016/j.earscirev.2024.104927_bb0440) 2009; 5 Huang (10.1016/j.earscirev.2024.104927_bb0350) 2020; 191 Kuhn (10.1016/j.earscirev.2024.104927_bb0420) Wislocki (10.1016/j.earscirev.2024.104927_bb0770) 1991; 40 Brenning (10.1016/j.earscirev.2024.104927_bb0135) 2012 Rumelhart (10.1016/j.earscirev.2024.104927_bb0630) 1986; 323 Sirbu (10.1016/j.earscirev.2024.104927_bb0700) 2023 Heckmann (10.1016/j.earscirev.2024.104927_bb0320) 2014; 14 Lee (10.1016/j.earscirev.2024.104927_bb0435) 2018; 303 Lombardo (10.1016/j.earscirev.2024.104927_bb0495) 2022; 36 Aguilera (10.1016/j.earscirev.2024.104927_bb0010) 2022; 36 Rossi (10.1016/j.earscirev.2024.104927_bb0625) 2022; 15 Buiter (10.1016/j.earscirev.2024.104927_bb0155) 2016; 92 ISRIC (10.1016/j.earscirev.2024.104927_bb0360) 2024 Dahal (10.1016/j.earscirev.2024.104927_bb0205) 2023; 176 Atkinson (10.1016/j.earscirev.2024.104927_bb0060) 1998; 24 Allen (10.1016/j.earscirev.2024.104927_bb0035) 1997 Rabby (10.1016/j.earscirev.2024.104927_bb0590) 2023; 13 Sin Yin (10.1016/j.earscirev.2024.104927_bb0685) 2010 Amato (10.1016/j.earscirev.2024.104927_bb0055) 2023; 82 Chen (10.1016/j.earscirev.2024.104927_bb0185) 2016 Brenning (10.1016/j.earscirev.2024.104927_bb0130) 2008 Leung (10.1016/j.earscirev.2024.104927_bb0445) 2024; 83 Wang (10.1016/j.earscirev.2024.104927_bb0760) 2024; 15 Das (10.1016/j.earscirev.2024.104927_bb0215) 2022; 115 Moreno (10.1016/j.earscirev.2024.104927_bb0550) 2023 Rolain (10.1016/j.earscirev.2024.104927_bb0615) 2023; 118 Kassambara (10.1016/j.earscirev.2024.104927_bb0395) Schlögel (10.1016/j.earscirev.2024.104927_bb0660) 2018; 301 Openshaw (10.1016/j.earscirev.2024.104927_bb0560) 1984 Bajni (10.1016/j.earscirev.2024.104927_bb0070) 2023 Bornaetxea (10.1016/j.earscirev.2024.104927_bb0100) 2018; 18 Marjanović (10.1016/j.earscirev.2024.104927_bb0520) 2011; 123 Carrara (10.1016/j.earscirev.2024.104927_bb0175) 1995 Süzen (10.1016/j.earscirev.2024.104927_bb0725) 2004; 71 Luzi (10.1016/j.earscirev.2024.104927_bb0510) 2000; 58 |
| References_xml | – volume: 92 start-page: 116 year: 2016 end-page: 139 ident: bb0670 article-title: Benchmarking analogue models of brittle thrust wedges publication-title: J. Struct. Geol. – volume: 92 start-page: 140 year: 2016 end-page: 177 ident: bb0155 article-title: Benchmarking numerical models of brittle thrust wedges publication-title: J. Struct. Geol. – volume: 191 year: 2020 ident: bb0350 article-title: Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping publication-title: Catena – volume: 13 start-page: 3339 year: 2013 end-page: 3355 ident: bb0515 article-title: Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression publication-title: Nat. Hazards Earth Syst. Sci. – volume: 358 year: 2020 ident: bb0045 article-title: Parameter–free delineation of slope units and terrain subdivision of Italy publication-title: Geomorphology – volume: 11 start-page: 425 year: 2014 end-page: 439 ident: bb0400 article-title: Landslide susceptibility mapping using GIS–based multi–criteria decision analysis, support vector machines, and logistic regression publication-title: Landslides – year: 2020 ident: bb0390 article-title: Geopandas/Geopandas: v0.8.1 – volume: 66 start-page: 327 year: 2005 end-page: 343 ident: bb0255 article-title: Artificial neural networks applied to landslide susceptibility assessment publication-title: Geomorphology – year: 2020 ident: bb0420 article-title: Tidymodels: A Collection of Packages for Modeling and Machine Learning Using Tidyverse Principles – volume: 40 start-page: 169 year: 1991 end-page: 172 ident: bb0770 article-title: An expert system for landslide hazard and risk assessment publication-title: Comput. Struct. – volume: 6 start-page: 687 year: 2006 end-page: 695 ident: bb0430 article-title: Earthquake–induced landslide–susceptibility mapping using an artificial neural network publication-title: Nat. Hazards Earth Syst. Sci. – volume: 123 start-page: 225 year: 2011 end-page: 234 ident: bb0520 article-title: Landslide susceptibility assessment using SVM machine learning algorithm publication-title: Eng. Geol. – volume: 786 year: 2021 ident: bb0165 article-title: Introducing intense rainfall and snowmelt variables to implement a process–related non–stationary shallow landslide susceptibility analysis publication-title: Sci. Total Environ. – volume: 13 start-page: 1740 year: 2023 ident: bb0590 article-title: An objective absence data sampling method for landslide susceptibility mapping publication-title: Sci. Rep. – year: 2023 ident: bb0740 article-title: Exploring the benchmark dataset for tasks related to landslide susceptibility assessment publication-title: EGU General Assembly 2023 – start-page: 23 year: 2008 end-page: 32 ident: bb0130 article-title: Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models publication-title: SAGA – Second out. Universität Hamburg Institut für Geographie. Volume 19 of Hamburger Beiträge zur Physischen Geographie und Landschaftsökologie – volume: 118 start-page: 2513 year: 2023 end-page: 2542 ident: bb0105 article-title: Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain) publication-title: Nat. Hazards – volume: 14 year: 2023 ident: bb0800 article-title: Ensemble learning framework for landslide susceptibility mapping: different basic classifier and ensemble strategy publication-title: Geosci. Front. – volume: 115 start-page: 172 year: 2010 end-page: 187 ident: bb0600 article-title: Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA publication-title: Geomorphology – volume: 21 start-page: 3940 year: 2005 end-page: 3941 ident: bb0695 article-title: ROCR: visualizing classifier performance in R publication-title: Bioinformatics – volume: 354 year: 2020 ident: bb0710 article-title: The (f)utility to account for pre–failure topography in data–driven landslide susceptibility modelling publication-title: Geomorphology – volume: 898 year: 2023 ident: bb0245 article-title: Assessing multi–hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example publication-title: Sci. Total Environ. – volume: 2 start-page: 1308 year: 2020 ident: bb0635 article-title: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest publication-title: SN Appl. Sci. – year: 2022 ident: bb0415 article-title: Tidy Modeling with R: A Framework for Modeling in the Tidyverse – volume: 82 start-page: 160 year: 2023 ident: bb0055 article-title: Earthquake–triggered landslide susceptibility in Italy by means of artificial neural network publication-title: Bull. Eng. Geol. Environ. – start-page: 205 year: 2006 end-page: 218 ident: bb0295 article-title: Appendix B: Cross–Validation, the Jackknife, and the Bootstrap: Excess Error Estimation in Forward Logistic Regression – volume: 193 year: 2020 ident: bb0095 article-title: The influence of the inventory on the determination of the rainfall–induced shallow landslides susceptibility using generalized additive models publication-title: CATENA – volume: 301 year: 2022 ident: bb0755 article-title: Space–time susceptibility modeling of hydro-morphological processes at the chinese national scale publication-title: Eng. Geol. – volume: 389 year: 2021 ident: bb0380 article-title: A global landslide non–susceptibility map publication-title: Geomorphology – year: 2023 ident: bb0550 article-title: Slope unit size matters - why should the areal extent of slope units be considered in data–driven landslide susceptibility models? publication-title: EGU General Assembly 2023 – volume: 776 year: 2021 ident: bb0715 article-title: Correlation does not imply geomorphic causation in data–driven landslide susceptibility modelling — benefits of exploring landslide data collection effects publication-title: Sci. Total Environ. – volume: 65 start-page: 1389 year: 1999 end-page: 1399 ident: bb0195 article-title: Probabilistic prediction models for landslide hazard mapping publication-title: Photogramm. Eng. Remote. Sens. – start-page: 5372 year: 2012 end-page: 5375 ident: bb0135 article-title: Spatial cross–validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest publication-title: 2012 IEEE International Geoscience and Remote Sensing Symposium – volume: 232 year: 2022 ident: bb0480 article-title: Landslide susceptibility maps of Italy: lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory publication-title: Earth Sci. Rev. – volume: 585 start-page: 357 year: 2020 end-page: 362 ident: bb0315 article-title: Array programming with Numpy publication-title: Nature – volume: 56 start-page: 1335 year: 2023 end-page: 1354 ident: bb0260 article-title: Space–time landslide susceptibility modeling based on data–driven methods publication-title: Math. Geosci. – volume: 135 year: 2024 ident: bb0235 article-title: Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping publication-title: J. S. Am. Earth Sci. – volume: 207 year: 2020 ident: bb0535 article-title: Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance publication-title: Earth Sci. Rev. – volume: 411 year: 2019 ident: bb0540 article-title: Importance of spatial predictor variable selection in machine learning applications - moving from data reproduction to spatial prediction publication-title: Ecol. Model. – volume: 176 year: 2023 ident: bb0205 article-title: Explainable artificial intelligence in geoscience: a glimpse into the future of landslide susceptibility modeling publication-title: Comput. Geosci. – volume: 6 start-page: 20 year: 2004 end-page: 29 ident: bb0075 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newslett. – volume: vol. 8 year: 1967 ident: bb0365 article-title: Cybernetics and forecasting techniques publication-title: Modern Analytic and Computational Methods in Science and Mathematics – year: 2022 ident: bb0280 article-title: Awesome Public Datasets – volume: 9 start-page: 1 year: 2016 end-page: 16 ident: bb0190 article-title: A GIS–based comparative study of frequency ratio, statistical index and weights–of–evidence models in landslide susceptibility mapping publication-title: Arab. J. Geosci. – start-page: 538 year: 2010 end-page: 547 ident: bb0685 article-title: Dichotomous Logistic Regression with Leave–One–Out Validation – year: 2023 ident: bb0015 article-title: Ensemble learning on the benchmark dataset for landslide susceptibility zonation in Central Italy publication-title: EGU General Assembly 2023 – year: 2013 ident: bb0340 article-title: Applied logistic regression publication-title: Wiley Series in Probability and Statistics – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bb0565 article-title: Scikit–learn: machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 27 start-page: 861 year: 2006 end-page: 874 ident: bb0270 article-title: An introduction to ROC analysis publication-title: Pattern Recogn. Lett. – year: 2023 ident: bb0645 article-title: Resolution of data, type of inventory and data splitting in machine learning-based landslide susceptibility mapping publication-title: EGU General Assembly 2023 – volume: 34 start-page: 667 year: 2002 end-page: 677 ident: bb0530 article-title: The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations publication-title: Appl. Econ. – volume: 303 start-page: 284 year: 2018 end-page: 298 ident: bb0435 article-title: Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods publication-title: Geomorphology – volume: 9 start-page: 1077 year: 2016 end-page: 1097 ident: bb0735 article-title: Spatial prediction of rainfall–induced shallow landslides using hybrid integration approach of Least–Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam publication-title: Int. J. Digit. Earth – year: 2017 ident: bb0775 article-title: Generalized Additive Models – An Introduction with R publication-title: Mathematics & Statistics – volume: 19 start-page: 1670 year: 2022 end-page: 1698 ident: bb0460 article-title: Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility publication-title: J. Mt. Sci. – volume: 170 year: 2022 ident: bb0410 article-title: Validation and inter–comparison of models for landslide tsunami generation publication-title: Ocean Model – volume: 35 start-page: 1267 year: 2020 end-page: 1292 ident: bb0730 article-title: A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers publication-title: Geocarto Int. – volume: 912 year: 2024 ident: bb0555 article-title: Space–time data–driven modeling of precipitation–induced shallow landslides in South Tyrol, Italy publication-title: Sci. Total Environ. – volume: 14 start-page: 95 year: 2014 end-page: 118 ident: bb0570 article-title: Assessing the quality of landslide susceptibility maps–case study Lower Austria publication-title: Nat. Hazards Earth Syst. Sci. – start-page: 135 year: 1995 end-page: 175 ident: bb0175 article-title: GIS technology in mapping landslide hazard publication-title: Geographical Information Systems in Assessing Natural Hazards – volume: 323 start-page: 533 year: 1986 end-page: 536 ident: bb0630 article-title: Learning representations by back–propagating errors publication-title: Nature – volume: 36 start-page: 2229 year: 2022 end-page: 2242 ident: bb0495 article-title: From scenario–based seismic hazard to scenario–based landslide hazard: fast–forwarding to the future via statistical simulations publication-title: Stoch. Env. Res. Risk A. – year: 2022 ident: bb0655 article-title: R Package ’GGally’: A Plotting System Based on the Grammar of Graphics – volume: 36 start-page: 2399 year: 2022 end-page: 2417 ident: bb0795 article-title: Review of landslide susceptibility assessment based on knowledge mapping publication-title: Stoch. Env. Res. Risk A. – volume: 12 year: 2017 ident: bb0325 article-title: SoilGrids250m: Global gridded soil information based on machine learning publication-title: PLoS One – volume: 83 start-page: 249 year: 2024 ident: bb0445 article-title: The use of digital technology for rock mass discontinuity mapping: review of benchmarking exercise publication-title: Bull. Eng. Geol. Environ. – volume: 356 year: 2020 ident: bb0370 article-title: Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach publication-title: Geomorphology – volume: 118 start-page: 2227 year: 2023 end-page: 2244 ident: bb0615 article-title: Influence of landslide inventory timespan and data selection on slope unit–based susceptibility models publication-title: Nat. Hazards – year: 2002 ident: bb0385 article-title: Principal Component Analysis – volume: 35 start-page: 179 year: 2019 end-page: 193 ident: bb0425 article-title: Current and future status of GIS–based landslide susceptibility mapping: a literature review publication-title: Korean J. Remote Sens. – year: 2009 ident: bb0805 article-title: Mixed Effects Models and Extensions in Ecology with R – volume: 71 start-page: 303 year: 2004 end-page: 321 ident: bb0725 article-title: Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey publication-title: Eng. Geol. – year: 2023 ident: bb0700 article-title: Landslide susceptibility model based on random forest classification publication-title: EGU General Assembly 2023 – volume: 18 start-page: 2455 year: 2018 end-page: 2469 ident: bb0100 article-title: Effective surveyed area and its role in statistical landslide susceptibility assessments publication-title: Nat. Hazards Earth Syst. Sci. – volume: 2 start-page: 18 year: 2002 end-page: 22 ident: bb0455 article-title: Classification and regression by randomForest publication-title: R News – year: 2020 ident: bb0030 article-title: PyCaret: an open source, low-code machine learning library in Python – volume: 7 start-page: 179 year: 1936 end-page: 188 ident: bb0275 article-title: The use of multiple measurements in taxonomic problems publication-title: Ann. Eugenics – year: 2017 ident: bb0405 article-title: Adam: A method for stochastic optimization – volume: 198 year: 2021 ident: bb0500 article-title: Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using artificial neural networks publication-title: Catena – ident: bb0585 article-title: R: A Language and Environment for Statistical Computing – volume: 105 start-page: 1348 year: 2018 ident: bb0595 article-title: Lasso regression publication-title: Br. J. Surg. – volume: 35 start-page: 1243 year: 2021 end-page: 1256 ident: bb0450 article-title: Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping publication-title: Stoch. Env. Res. Risk A. – year: 1984 ident: bb0560 article-title: The modifiable areal unit problem publication-title: Concepts and Techniques in Modern Geography N. 38, Geo Books – Norwick – volume: 81 start-page: 1 year: 2015 end-page: 11 ident: bb0290 article-title: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling publication-title: Comput. Geosci. – volume: 9 start-page: 3975 year: 2016 end-page: 3991 ident: bb0040 article-title: Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling publication-title: Geosci. Model Dev. – volume: 112 start-page: 42 year: 2012 end-page: 66 ident: bb0310 article-title: Landslide inventory maps: New tools for an old problem publication-title: Earth Sci. Rev. – volume: 14 start-page: 259 year: 2014 end-page: 278 ident: bb0320 article-title: Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows publication-title: Nat. Hazards Earth Syst. Sci. – volume: 11 year: 2021 ident: bb0575 article-title: Assessment of earthquake–induced landslide inventories and susceptibility maps using slope unit–based logistic regression and geospatial statistics publication-title: Sci. Rep. – start-page: 127 year: 2022 end-page: 140 ident: bb0080 article-title: Spatial cross–validation for globally distributed data publication-title: Discovery Science – volume: 116 start-page: 274 year: 2010 end-page: 283 ident: bb0785 article-title: Landslide susceptibility mapping in Injae, Korea, using a decision tree publication-title: Eng. Geol. – volume: 11 year: 2021 ident: bb0240 article-title: Landslide susceptibility mapping in Brazil: a review publication-title: Geosciences – volume: 126 year: 2024 ident: bb0265 article-title: Landslide hazard spatiotemporal prediction based on data-driven models: estimating where, when and how large landslide may be publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 9 start-page: 93 year: 2012 end-page: 106 ident: bb0020 article-title: A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey publication-title: Landslides – start-page: 233 year: 2006 end-page: 240 ident: bb0220 article-title: The relationship between precision–recall and ROC curves publication-title: Proceedings of the 23rd International Conference on Machine Learning – start-page: 278 year: 1995 end-page: 282 ident: bb0330 article-title: Random decision forests publication-title: Proceedings of 3rd International Conference on Document Analysis and Recognition – year: 2023 ident: bb0545 article-title: Bayesian logistic regression and optimized XGBoost models for landslide susceptibility assessment publication-title: EGU General Assembly 2023 – year: 2021 ident: bb0765 article-title: R Package ‘Corrplot’: Visualization of a Correlation Matrix – volume: 260 year: 2019 ident: bb0050 article-title: Accounting for covariate distributions in slope–unit–based landslide susceptibility models. A case study in the alpine environment publication-title: Eng. Geol. – start-page: 56 year: 2010 end-page: 61 ident: bb0525 article-title: Data structures for statistical computing in Python publication-title: Proceedings of the 9th Python in Science Conference – volume: 16 start-page: 427 year: 1991 end-page: 445 ident: bb0170 article-title: GIS techniques and statistical models in evaluating landslide hazard publication-title: Earth Surf. Process. Landf. – volume: 45 start-page: 5 year: 2011 end-page: 32 ident: bb0125 article-title: Random forests publication-title: Mach. Learn. – year: 2023 ident: bb0070 article-title: A novel dynamic rockfall susceptibility model including precipitation, temperature and snowmelt predictors: a case study in Aosta Valley publication-title: Landslides – volume: 115 start-page: 23 year: 2022 end-page: 72 ident: bb0215 article-title: A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya publication-title: Nat. Hazards – volume: 79 start-page: 251 year: 2005 end-page: 266 ident: bb0790 article-title: Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey) publication-title: Eng. Geol. – year: 2023 ident: bb0650 article-title: Landslide susceptibility mapping via binomial generalized additive model publication-title: EGU General Assembly 2023 – volume: 15 start-page: 5651 year: 2022 end-page: 5666 ident: bb0625 article-title: LAND–SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation publication-title: Geosci. Model Dev. – volume: 7 start-page: 455 year: 2010 end-page: 470 ident: bb0745 article-title: Quality assessment of the Italian landslide inventory using GIS processing publication-title: Landslides – volume: 262 start-page: 8 year: 2016 end-page: 23 ident: bb0705 article-title: Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps publication-title: Geomorphology – volume: 58 start-page: 2283 year: 2023 end-page: 2301 ident: bb0475 article-title: A comprehensive review of machine learning–based methods in landslide susceptibility mapping publication-title: Geol. J. – year: 2023 ident: bb0110 article-title: Application of the LAND–SUITE software with a benchmark dataset for landslide susceptibility zonation publication-title: EGU General Assembly 2023 – volume: 15 year: 2024 ident: bb0760 article-title: From spatio-temporal landslide susceptibility to landslide risk forecast publication-title: Geosci. Front. – year: 1987 ident: bb0505 article-title: Slope Stability Assessment under Uncertainty: A First Order Stochastic Approach – volume: 123 year: 2020 ident: bb0120 article-title: Landslide susceptibility mapping with r.landslide: a free open–source GIS–integrated tool based on artificial neural networks publication-title: Environ. Model Softw. – volume: 54 start-page: 1127 year: 2008 end-page: 1143 ident: bb0025 article-title: Landslide susceptibility mapping for a landslide–prone area (Findikli, NE of Turkey) by likelihood–frequency ratio and weighted linear combination models publication-title: Environ. Geol. – year: 1972 ident: bb0115 article-title: MG 1972. Landslide susceptibility in San Mateo County, California – volume: 12 start-page: 77 year: 2011 ident: bb0610 article-title: pROC: an open–source package for R and S+ to analyze and compare ROC curves publication-title: BMC Bioinformatics – start-page: 171 year: 1990 end-page: 183 ident: bb0090 article-title: Weights of evidence modelling: A new approach to mapping mineral potential publication-title: Statistical Applications in the Earth Sciences – volume: 269 start-page: 9 year: 2013 end-page: 17 ident: bb0680 article-title: Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes publication-title: Ecol. Model. – year: 1997 ident: bb0225 article-title: Bootstrap Methods and their Application publication-title: Cambridge Series in Statistical and Probabilistic Mathematics – volume: 81 start-page: 166 year: 2006 end-page: 184 ident: bb0305 article-title: Estimating the quality of landslide susceptibility models publication-title: Geomorphology – volume: 301 start-page: 10 year: 2018 end-page: 20 ident: bb0660 article-title: Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models publication-title: Geomorphology – volume: 24 start-page: 823 year: 2024 end-page: 845 ident: bb0210 article-title: Space–time landslide hazard modeling via ensemble neural networks publication-title: Nat. Hazards Earth Syst. Sci. – volume: 180 start-page: 60 year: 2018 end-page: 91 ident: bb0605 article-title: A review of statistically–based landslide susceptibility models publication-title: Earth Sci. Rev. – start-page: 785 year: 2016 end-page: 794 ident: bb0185 article-title: XGBoost: a scalable tree boosting system publication-title: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – year: 2023 ident: bb0485 article-title: Landslide susceptibility within the binomial generalized additive model publication-title: EGU General Assembly 2023 – volume: 36 start-page: 2031 year: 2022 end-page: 2048 ident: bb0010 article-title: On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks publication-title: Stoch. Env. Res. Risk A. – volume: 63 start-page: 1 year: 2015 end-page: 25 ident: bb0465 article-title: Bayesian spatial modelling with R-INLA publication-title: J. Stat. Softw. – volume: 125 start-page: 51 year: 2011 end-page: 61 ident: bb0720 article-title: Spatial agreement of predicted patterns in landslide susceptibility maps publication-title: Geomorphology – year: 2023 ident: bb0085 article-title: rgdal: Bindings for the ‘Geospatial’ Data Abstraction Library – volume: 104 start-page: 62 year: 2017 end-page: 74 ident: bb0250 article-title: Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods publication-title: Comput. Geosci. – volume: 165 start-page: 520 year: 2018 end-page: 529 ident: bb0345 article-title: Review on landslide susceptibility mapping using support vector machines publication-title: Catena – year: 2020 ident: bb0395 article-title: Factoextra: Extract and Visualize the Results of Multivariate Data Analyses – volume: 129 start-page: 376 year: 2011 end-page: 386 ident: bb0285 article-title: Integrating physical and empirical landslide susceptibility models using generalized additive models publication-title: Geomorphology – year: 2013 ident: bb0375 article-title: An Introduction to Statistical Learning with Applications in R – volume: 244 start-page: 14 year: 2018 end-page: 24 ident: bb0490 article-title: Presenting logistic regression–based landslide susceptibility results publication-title: Eng. Geol. – volume: 24 start-page: 373 year: 1998 end-page: 385 ident: bb0060 article-title: Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy publication-title: Comput. Geosci. – volume: 30 start-page: 451 year: 2003 end-page: 472 ident: bb0200 article-title: Validation of spatial prediction models for landslide hazard mapping publication-title: Nat. Hazards – volume: 37 start-page: 14309 year: 2022 end-page: 14334 ident: bb0470 article-title: A bibliometric analysis of the landslide susceptibility research (1999–2021) publication-title: Geocarto Int. – volume: 406 start-page: 109 year: 2019 end-page: 120 ident: bb0665 article-title: Hyperparameter tuning and performance assessment of statistical and machine–learning algorithms using spatial data publication-title: Ecol. Model. – volume: 415 year: 2022 ident: bb0065 article-title: Assessing the utility of regionalized rock–mass geomechanical properties in rockfall susceptibility modelling in an alpine environment publication-title: Geomorphology – volume: 31 start-page: 181 year: 1999 end-page: 216 ident: bb0300 article-title: Landslide hazard evaluation: a review of current techniques and their application in a multi–scale study, Central Italy publication-title: Geomorphology – volume: 157 start-page: 157 year: 2002 end-page: 177 ident: bb0780 article-title: GAMs with integrated model selection using penalized regression splines and applications to environmental modelling publication-title: Ecol. Model. – volume: 7 year: 2020 ident: bb0675 article-title: Landslide susceptibility evaluation and hazard zonation techniques – a review publication-title: Geoenviron. Disasters – volume: 12 start-page: 346 year: 2020 ident: bb0580 article-title: Mapping landslides on eo data: Performance of deep learning models vs. traditional machine learning models publication-title: Remote Sens. – volume: 79 start-page: 2799 year: 2020 end-page: 2814 ident: bb0180 article-title: Exploring spatial non–stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression publication-title: Bull. Eng. Geol. Environ. – year: 2024 ident: bb0360 article-title: Wosis, world soil profile database – year: 2023 ident: bb0640 article-title: Cross validation technique preference for landslide susceptibility zoning based on slope unit and machine learning workflow publication-title: EGU General Assembly 2023 – volume: 5 start-page: 87 year: 2009 end-page: 93 ident: bb0440 article-title: GIS methodology to assess landslide susceptibility: application to a river catchment of Central Italy publication-title: J. Maps – volume: 78 start-page: 1 year: 1950 end-page: 3 ident: bb0140 article-title: Verification of forecasts expressed in terms of probability publication-title: Mon. Weather Rev. – volume: 104700 year: 2024 ident: bb0355 article-title: Modelling landslide susceptibility prediction: a review and construction of semi–supervised imbalanced theory publication-title: Earth Sci. Rev. – volume: 9 start-page: 3533 year: 2016 end-page: 3543 ident: bb0620 article-title: LAND–SE: a software for statistically based landslide susceptibility zonation, version 1.0 publication-title: Geosci. Model Dev. – volume: 12 start-page: 419 year: 2015 end-page: 436 ident: bb0150 article-title: A systematic review of landslide probability mapping using logistic regression publication-title: Landslides – volume: 176 start-page: 45 year: 2019 end-page: 64 ident: bb0335 article-title: Exploring the effects of the design and quantity of absence data on the performance of random forest–based landslide susceptibility mapping publication-title: Catena – volume: 14 start-page: 4129 year: 2022 end-page: 4151 ident: bb0145 article-title: A new digital lithological map of Italy at 1:100,000 scale for geo–mechanical modelling publication-title: Earth Syst. Sci. Data – year: 2024 ident: bb0750 article-title: Machine learning repository – start-page: 1 year: 1990 end-page: 21 ident: bb0005 article-title: Statistical pattern integration for mineral exploration publication-title: Computer Applications in Resource Estimation – year: 1997 ident: bb0035 article-title: Understanding Regression Analysis – year: 2023 ident: bb0160 article-title: Comparison of the effectiveness of application of gams for landslide susceptibility modelling in Apennine and Alpine areas publication-title: EGU General Assembly 2023 – volume: 119 start-page: 1513 year: 2023 end-page: 1530 ident: bb0230 article-title: On the estimation of landslide intensity, hazard and density via data–driven models publication-title: Nat. Hazards – volume: 58 start-page: 313 year: 2000 end-page: 336 ident: bb0510 article-title: Slope vulnerability to earthquakes at subregional scale, using probabilistic techniques and geographic information systems publication-title: Eng. Geol. – year: 2023 ident: bb0690 article-title: A slope units based landslide susceptibility analyses using weight of evidence and random forest publication-title: EGU General Assembly 2023 – volume: 31 start-page: 181 year: 1999 ident: 10.1016/j.earscirev.2024.104927_bb0300 article-title: Landslide hazard evaluation: a review of current techniques and their application in a multi–scale study, Central Italy publication-title: Geomorphology doi: 10.1016/S0169-555X(99)00078-1 – volume: 21 start-page: 3940 year: 2005 ident: 10.1016/j.earscirev.2024.104927_bb0695 article-title: ROCR: visualizing classifier performance in R publication-title: Bioinformatics doi: 10.1093/bioinformatics/bti623 – volume: 118 start-page: 2227 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0615 article-title: Influence of landslide inventory timespan and data selection on slope unit–based susceptibility models publication-title: Nat. Hazards doi: 10.1007/s11069-023-06092-w – volume: 81 start-page: 1 year: 2015 ident: 10.1016/j.earscirev.2024.104927_bb0290 article-title: Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2015.04.007 – volume: 411 year: 2019 ident: 10.1016/j.earscirev.2024.104927_bb0540 article-title: Importance of spatial predictor variable selection in machine learning applications - moving from data reproduction to spatial prediction publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2019.108815 – volume: 14 start-page: 259 year: 2014 ident: 10.1016/j.earscirev.2024.104927_bb0320 article-title: Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-14-259-2014 – volume: 40 start-page: 169 year: 1991 ident: 10.1016/j.earscirev.2024.104927_bb0770 article-title: An expert system for landslide hazard and risk assessment publication-title: Comput. Struct. doi: 10.1016/0045-7949(91)90469-3 – start-page: 171 year: 1990 ident: 10.1016/j.earscirev.2024.104927_bb0090 article-title: Weights of evidence modelling: A new approach to mapping mineral potential – volume: 170 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0410 article-title: Validation and inter–comparison of models for landslide tsunami generation publication-title: Ocean Model doi: 10.1016/j.ocemod.2021.101943 – volume: 354 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0710 article-title: The (f)utility to account for pre–failure topography in data–driven landslide susceptibility modelling publication-title: Geomorphology doi: 10.1016/j.geomorph.2020.107041 – year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0750 – start-page: 205 year: 2006 ident: 10.1016/j.earscirev.2024.104927_bb0295 – volume: 45 start-page: 5 year: 2011 ident: 10.1016/j.earscirev.2024.104927_bb0125 article-title: Random forests publication-title: Mach. Learn. doi: 10.1023/A:1010933404324 – volume: 36 start-page: 2399 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0795 article-title: Review of landslide susceptibility assessment based on knowledge mapping publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-021-02165-z – volume: 123 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0120 article-title: Landslide susceptibility mapping with r.landslide: a free open–source GIS–integrated tool based on artificial neural networks publication-title: Environ. Model Softw. doi: 10.1016/j.envsoft.2019.104565 – volume: 15 start-page: 5651 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0625 article-title: LAND–SUITE V1.0: a suite of tools for statistically based landslide susceptibility zonation publication-title: Geosci. Model Dev. doi: 10.5194/gmd-15-5651-2022 – volume: 79 start-page: 2799 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0180 article-title: Exploring spatial non–stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression publication-title: Bull. Eng. Geol. Environ. doi: 10.1007/s10064-020-01733-x – start-page: 56 year: 2010 ident: 10.1016/j.earscirev.2024.104927_bb0525 article-title: Data structures for statistical computing in Python doi: 10.25080/Majora-92bf1922-00a – volume: 54 start-page: 1127 year: 2008 ident: 10.1016/j.earscirev.2024.104927_bb0025 article-title: Landslide susceptibility mapping for a landslide–prone area (Findikli, NE of Turkey) by likelihood–frequency ratio and weighted linear combination models publication-title: Environ. Geol. doi: 10.1007/s00254-007-0882-8 – volume: 24 start-page: 823 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0210 article-title: Space–time landslide hazard modeling via ensemble neural networks publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-24-823-2024 – volume: 898 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0245 article-title: Assessing multi–hazard susceptibility to cryospheric hazards: lesson learnt from an Alaskan example publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2023.165289 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0690 article-title: A slope units based landslide susceptibility analyses using weight of evidence and random forest – volume: 34 start-page: 667 year: 2002 ident: 10.1016/j.earscirev.2024.104927_bb0530 article-title: The impact of collinearity on regression analysis: the asymmetric effect of negative and positive correlations publication-title: Appl. Econ. doi: 10.1080/00036840110058482 – start-page: 538 year: 2010 ident: 10.1016/j.earscirev.2024.104927_bb0685 – year: 2013 ident: 10.1016/j.earscirev.2024.104927_bb0375 – start-page: 278 year: 1995 ident: 10.1016/j.earscirev.2024.104927_bb0330 article-title: Random decision forests – year: 2017 ident: 10.1016/j.earscirev.2024.104927_bb0405 – ident: 10.1016/j.earscirev.2024.104927_bb0765 – start-page: 233 year: 2006 ident: 10.1016/j.earscirev.2024.104927_bb0220 article-title: The relationship between precision–recall and ROC curves – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0070 article-title: A novel dynamic rockfall susceptibility model including precipitation, temperature and snowmelt predictors: a case study in Aosta Valley publication-title: Landslides doi: 10.1007/s10346-023-02091-x – volume: 14 start-page: 4129 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0145 article-title: A new digital lithological map of Italy at 1:100,000 scale for geo–mechanical modelling publication-title: Earth Syst. Sci. Data doi: 10.5194/essd-14-4129-2022 – volume: 12 year: 2017 ident: 10.1016/j.earscirev.2024.104927_bb0325 article-title: SoilGrids250m: Global gridded soil information based on machine learning publication-title: PLoS One doi: 10.1371/journal.pone.0169748 – volume: 35 start-page: 1243 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0450 article-title: Application and comparison of different ensemble learning machines combining with a novel sampling strategy for shallow landslide susceptibility mapping publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-020-01893-y – volume: 81 start-page: 166 year: 2006 ident: 10.1016/j.earscirev.2024.104927_bb0305 article-title: Estimating the quality of landslide susceptibility models publication-title: Geomorphology doi: 10.1016/j.geomorph.2006.04.007 – volume: 11 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0575 article-title: Assessment of earthquake–induced landslide inventories and susceptibility maps using slope unit–based logistic regression and geospatial statistics publication-title: Sci. Rep. doi: 10.1038/s41598-021-00780-y – volume: 269 start-page: 9 year: 2013 ident: 10.1016/j.earscirev.2024.104927_bb0680 article-title: Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2013.08.011 – volume: 116 start-page: 274 year: 2010 ident: 10.1016/j.earscirev.2024.104927_bb0785 article-title: Landslide susceptibility mapping in Injae, Korea, using a decision tree publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2010.09.009 – volume: 9 start-page: 93 year: 2012 ident: 10.1016/j.earscirev.2024.104927_bb0020 article-title: A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir, Turkey publication-title: Landslides doi: 10.1007/s10346-011-0283-7 – volume: 776 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0715 article-title: Correlation does not imply geomorphic causation in data–driven landslide susceptibility modelling — benefits of exploring landslide data collection effects publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2021.145935 – volume: 79 start-page: 251 year: 2005 ident: 10.1016/j.earscirev.2024.104927_bb0790 article-title: Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey) publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2005.02.002 – year: 1997 ident: 10.1016/j.earscirev.2024.104927_bb0035 – volume: 2 start-page: 1308 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0635 article-title: Assessing the predictive capability of ensemble tree methods for landslide susceptibility mapping using XGBoost, gradient boosting machine, and random forest publication-title: SN Appl. Sci. doi: 10.1007/s42452-020-3060-1 – volume: 92 start-page: 116 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0670 article-title: Benchmarking analogue models of brittle thrust wedges publication-title: J. Struct. Geol. doi: 10.1016/j.jsg.2016.03.005 – volume: 165 start-page: 520 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0345 article-title: Review on landslide susceptibility mapping using support vector machines publication-title: Catena doi: 10.1016/j.catena.2018.03.003 – volume: 13 start-page: 1740 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0590 article-title: An objective absence data sampling method for landslide susceptibility mapping publication-title: Sci. Rep. doi: 10.1038/s41598-023-28991-5 – volume: 301 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0755 article-title: Space–time susceptibility modeling of hydro-morphological processes at the chinese national scale publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2022.106586 – volume: 58 start-page: 2283 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0475 article-title: A comprehensive review of machine learning–based methods in landslide susceptibility mapping publication-title: Geol. J. doi: 10.1002/gj.4666 – ident: 10.1016/j.earscirev.2024.104927_bb0395 – volume: 9 start-page: 3533 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0620 article-title: LAND–SE: a software for statistically based landslide susceptibility zonation, version 1.0 publication-title: Geosci. Model Dev. doi: 10.5194/gmd-9-3533-2016 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0550 article-title: Slope unit size matters - why should the areal extent of slope units be considered in data–driven landslide susceptibility models? – ident: 10.1016/j.earscirev.2024.104927_bb0280 – year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0030 – volume: 63 start-page: 1 year: 2015 ident: 10.1016/j.earscirev.2024.104927_bb0465 article-title: Bayesian spatial modelling with R-INLA publication-title: J. Stat. Softw. doi: 10.18637/jss.v063.i19 – volume: 12 start-page: 346 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0580 article-title: Mapping landslides on eo data: Performance of deep learning models vs. traditional machine learning models publication-title: Remote Sens. doi: 10.3390/rs12030346 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0700 article-title: Landslide susceptibility model based on random forest classification – start-page: 23 year: 2008 ident: 10.1016/j.earscirev.2024.104927_bb0130 article-title: Statistical geocomputing combining R and SAGA: The example of landslide susceptibility analysis with generalized additive models – volume: 176 start-page: 45 year: 2019 ident: 10.1016/j.earscirev.2024.104927_bb0335 article-title: Exploring the effects of the design and quantity of absence data on the performance of random forest–based landslide susceptibility mapping publication-title: Catena doi: 10.1016/j.catena.2018.12.035 – volume: 92 start-page: 140 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0155 article-title: Benchmarking numerical models of brittle thrust wedges publication-title: J. Struct. Geol. doi: 10.1016/j.jsg.2016.03.003 – year: 2017 ident: 10.1016/j.earscirev.2024.104927_bb0775 article-title: Generalized Additive Models – An Introduction with R – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0740 article-title: Exploring the benchmark dataset for tasks related to landslide susceptibility assessment – volume: 24 start-page: 373 year: 1998 ident: 10.1016/j.earscirev.2024.104927_bb0060 article-title: Generalised linear modelling of susceptibility to landsliding in the Central Apennines, Italy publication-title: Comput. Geosci. doi: 10.1016/S0098-3004(97)00117-9 – year: 1987 ident: 10.1016/j.earscirev.2024.104927_bb0505 – volume: 406 start-page: 109 year: 2019 ident: 10.1016/j.earscirev.2024.104927_bb0665 article-title: Hyperparameter tuning and performance assessment of statistical and machine–learning algorithms using spatial data publication-title: Ecol. Model. doi: 10.1016/j.ecolmodel.2019.06.002 – ident: 10.1016/j.earscirev.2024.104927_bb0085 – volume: 65 start-page: 1389 year: 1999 ident: 10.1016/j.earscirev.2024.104927_bb0195 article-title: Probabilistic prediction models for landslide hazard mapping publication-title: Photogramm. Eng. Remote. Sens. – start-page: 127 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0080 article-title: Spatial cross–validation for globally distributed data – volume: 5 start-page: 87 year: 2009 ident: 10.1016/j.earscirev.2024.104927_bb0440 article-title: GIS methodology to assess landslide susceptibility: application to a river catchment of Central Italy publication-title: J. Maps doi: 10.4113/jom.2009.1041 – start-page: 135 year: 1995 ident: 10.1016/j.earscirev.2024.104927_bb0175 article-title: GIS technology in mapping landslide hazard – volume: 786 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0165 article-title: Introducing intense rainfall and snowmelt variables to implement a process–related non–stationary shallow landslide susceptibility analysis publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2021.147360 – year: 1972 ident: 10.1016/j.earscirev.2024.104927_bb0115 – volume: 176 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0205 article-title: Explainable artificial intelligence in geoscience: a glimpse into the future of landslide susceptibility modeling publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2023.105364 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0640 article-title: Cross validation technique preference for landslide susceptibility zoning based on slope unit and machine learning workflow – volume: 191 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0350 article-title: Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping publication-title: Catena doi: 10.1016/j.catena.2020.104580 – volume: 115 start-page: 23 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0215 article-title: A critical review on landslide susceptibility zonation: recent trends, techniques, and practices in Indian Himalaya publication-title: Nat. Hazards doi: 10.1007/s11069-022-05554-x – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0545 article-title: Bayesian logistic regression and optimized XGBoost models for landslide susceptibility assessment – volume: 18 start-page: 2455 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0100 article-title: Effective surveyed area and its role in statistical landslide susceptibility assessments publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-18-2455-2018 – volume: 135 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0235 article-title: Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping publication-title: J. S. Am. Earth Sci. doi: 10.1016/j.jsames.2024.104805 – ident: 10.1016/j.earscirev.2024.104927_bb0420 – volume: 415 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0065 article-title: Assessing the utility of regionalized rock–mass geomechanical properties in rockfall susceptibility modelling in an alpine environment publication-title: Geomorphology doi: 10.1016/j.geomorph.2022.108401 – volume: 6 start-page: 687 year: 2006 ident: 10.1016/j.earscirev.2024.104927_bb0430 article-title: Earthquake–induced landslide–susceptibility mapping using an artificial neural network publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-6-687-2006 – volume: 12 start-page: 77 year: 2011 ident: 10.1016/j.earscirev.2024.104927_bb0610 article-title: pROC: an open–source package for R and S+ to analyze and compare ROC curves publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-12-77 – year: 2002 ident: 10.1016/j.earscirev.2024.104927_bb0385 – volume: 11 start-page: 425 year: 2014 ident: 10.1016/j.earscirev.2024.104927_bb0400 article-title: Landslide susceptibility mapping using GIS–based multi–criteria decision analysis, support vector machines, and logistic regression publication-title: Landslides doi: 10.1007/s10346-013-0391-7 – year: 1984 ident: 10.1016/j.earscirev.2024.104927_bb0560 article-title: The modifiable areal unit problem – volume: 11 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0240 article-title: Landslide susceptibility mapping in Brazil: a review publication-title: Geosciences doi: 10.3390/geosciences11100425 – volume: 303 start-page: 284 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0435 article-title: Modeling landslide susceptibility in data-scarce environments using optimized data mining and statistical methods publication-title: Geomorphology doi: 10.1016/j.geomorph.2017.12.007 – volume: 104700 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0355 article-title: Modelling landslide susceptibility prediction: a review and construction of semi–supervised imbalanced theory publication-title: Earth Sci. Rev. – volume: 19 start-page: 1670 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0460 article-title: Literature review and bibliometric analysis on data-driven assessment of landslide susceptibility publication-title: J. Mt. Sci. doi: 10.1007/s11629-021-7254-9 – volume: 232 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0480 article-title: Landslide susceptibility maps of Italy: lesson learnt from dealing with multiple landslide types and the uneven spatial distribution of the national inventory publication-title: Earth Sci. Rev. doi: 10.1016/j.earscirev.2022.104125 – volume: 260 year: 2019 ident: 10.1016/j.earscirev.2024.104927_bb0050 article-title: Accounting for covariate distributions in slope–unit–based landslide susceptibility models. A case study in the alpine environment publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2019.105237 – year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0390 – volume: 6 start-page: 20 year: 2004 ident: 10.1016/j.earscirev.2024.104927_bb0075 article-title: A study of the behavior of several methods for balancing machine learning training data publication-title: ACM SIGKDD Explor. Newslett. doi: 10.1145/1007730.1007735 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0645 article-title: Resolution of data, type of inventory and data splitting in machine learning-based landslide susceptibility mapping – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0650 article-title: Landslide susceptibility mapping via binomial generalized additive model – year: 1997 ident: 10.1016/j.earscirev.2024.104927_bb0225 article-title: Bootstrap Methods and their Application doi: 10.1017/CBO9780511802843 – volume: 262 start-page: 8 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0705 article-title: Exploring discrepancies between quantitative validation results and the geomorphic plausibility of statistical landslide susceptibility maps publication-title: Geomorphology doi: 10.1016/j.geomorph.2016.03.015 – volume: 2 start-page: 18 year: 2002 ident: 10.1016/j.earscirev.2024.104927_bb0455 article-title: Classification and regression by randomForest publication-title: R News – volume: 71 start-page: 303 year: 2004 ident: 10.1016/j.earscirev.2024.104927_bb0725 article-title: Data driven bivariate landslide susceptibility assessment using geographical information systems: a method and application to Asarsuyu catchment, Turkey publication-title: Eng. Geol. doi: 10.1016/S0013-7952(03)00143-1 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0110 article-title: Application of the LAND–SUITE software with a benchmark dataset for landslide susceptibility zonation – volume: 301 start-page: 10 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0660 article-title: Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models publication-title: Geomorphology doi: 10.1016/j.geomorph.2017.10.018 – volume: 157 start-page: 157 year: 2002 ident: 10.1016/j.earscirev.2024.104927_bb0780 article-title: GAMs with integrated model selection using penalized regression splines and applications to environmental modelling publication-title: Ecol. Model. doi: 10.1016/S0304-3800(02)00193-X – volume: 35 start-page: 179 year: 2019 ident: 10.1016/j.earscirev.2024.104927_bb0425 article-title: Current and future status of GIS–based landslide susceptibility mapping: a literature review publication-title: Korean J. Remote Sens. – volume: 198 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0500 article-title: Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using artificial neural networks publication-title: Catena doi: 10.1016/j.catena.2020.105067 – volume: 356 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0370 article-title: Regional susceptibility assessments with heterogeneous landslide information: Slope unit- vs. pixel-based approach publication-title: Geomorphology doi: 10.1016/j.geomorph.2020.107084 – volume: 9 start-page: 1077 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0735 article-title: Spatial prediction of rainfall–induced shallow landslides using hybrid integration approach of Least–Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam publication-title: Int. J. Digit. Earth doi: 10.1080/17538947.2016.1169561 – volume: 14 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0800 article-title: Ensemble learning framework for landslide susceptibility mapping: different basic classifier and ensemble strategy publication-title: Geosci. Front. doi: 10.1016/j.gsf.2023.101645 – volume: 585 start-page: 357 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0315 article-title: Array programming with Numpy publication-title: Nature doi: 10.1038/s41586-020-2649-2 – year: 2009 ident: 10.1016/j.earscirev.2024.104927_bb0805 – volume: 58 start-page: 313 year: 2000 ident: 10.1016/j.earscirev.2024.104927_bb0510 article-title: Slope vulnerability to earthquakes at subregional scale, using probabilistic techniques and geographic information systems publication-title: Eng. Geol. doi: 10.1016/S0013-7952(00)00041-7 – volume: 118 start-page: 2513 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0105 article-title: Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain) publication-title: Nat. Hazards doi: 10.1007/s11069-023-06103-w – volume: 126 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0265 article-title: Landslide hazard spatiotemporal prediction based on data-driven models: estimating where, when and how large landslide may be publication-title: Int. J. Appl. Earth Obs. Geoinf. – volume: 27 start-page: 861 year: 2006 ident: 10.1016/j.earscirev.2024.104927_bb0270 article-title: An introduction to ROC analysis publication-title: Pattern Recogn. Lett. doi: 10.1016/j.patrec.2005.10.010 – volume: 56 start-page: 1335 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0260 article-title: Space–time landslide susceptibility modeling based on data–driven methods publication-title: Math. Geosci. doi: 10.1007/s11004-023-10105-6 – volume: 112 start-page: 42 year: 2012 ident: 10.1016/j.earscirev.2024.104927_bb0310 article-title: Landslide inventory maps: New tools for an old problem publication-title: Earth Sci. Rev. doi: 10.1016/j.earscirev.2012.02.001 – volume: 13 start-page: 3339 year: 2013 ident: 10.1016/j.earscirev.2024.104927_bb0515 article-title: Assessing the spatial variability of coefficients of landslide predictors in different regions of Romania using logistic regression publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-13-3339-2013 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0160 article-title: Comparison of the effectiveness of application of gams for landslide susceptibility modelling in Apennine and Alpine areas – volume: 125 start-page: 51 year: 2011 ident: 10.1016/j.earscirev.2024.104927_bb0720 article-title: Spatial agreement of predicted patterns in landslide susceptibility maps publication-title: Geomorphology doi: 10.1016/j.geomorph.2010.09.004 – volume: 115 start-page: 172 year: 2010 ident: 10.1016/j.earscirev.2024.104927_bb0600 article-title: Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA publication-title: Geomorphology doi: 10.1016/j.geomorph.2009.10.002 – volume: 14 start-page: 95 year: 2014 ident: 10.1016/j.earscirev.2024.104927_bb0570 article-title: Assessing the quality of landslide susceptibility maps–case study Lower Austria publication-title: Nat. Hazards Earth Syst. Sci. doi: 10.5194/nhess-14-95-2014 – volume: 12 start-page: 419 year: 2015 ident: 10.1016/j.earscirev.2024.104927_bb0150 article-title: A systematic review of landslide probability mapping using logistic regression publication-title: Landslides doi: 10.1007/s10346-014-0550-5 – volume: 35 start-page: 1267 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0730 article-title: A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers publication-title: Geocarto Int. doi: 10.1080/10106049.2018.1559885 – volume: 358 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0045 article-title: Parameter–free delineation of slope units and terrain subdivision of Italy publication-title: Geomorphology doi: 10.1016/j.geomorph.2020.107124 – volume: 30 start-page: 451 year: 2003 ident: 10.1016/j.earscirev.2024.104927_bb0200 article-title: Validation of spatial prediction models for landslide hazard mapping publication-title: Nat. Hazards doi: 10.1023/B:NHAZ.0000007172.62651.2b – volume: 7 start-page: 455 year: 2010 ident: 10.1016/j.earscirev.2024.104927_bb0745 article-title: Quality assessment of the Italian landslide inventory using GIS processing publication-title: Landslides doi: 10.1007/s10346-010-0213-0 – volume: 119 start-page: 1513 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0230 article-title: On the estimation of landslide intensity, hazard and density via data–driven models publication-title: Nat. Hazards doi: 10.1007/s11069-023-06153-0 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.earscirev.2024.104927_bb0565 article-title: Scikit–learn: machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 104 start-page: 62 year: 2017 ident: 10.1016/j.earscirev.2024.104927_bb0250 article-title: Analysis of training sample selection strategies for regression-based quantitative landslide susceptibility mapping methods publication-title: Comput. Geosci. doi: 10.1016/j.cageo.2017.03.022 – volume: 389 year: 2021 ident: 10.1016/j.earscirev.2024.104927_bb0380 article-title: A global landslide non–susceptibility map publication-title: Geomorphology doi: 10.1016/j.geomorph.2021.107804 – start-page: 1 year: 1990 ident: 10.1016/j.earscirev.2024.104927_bb0005 article-title: Statistical pattern integration for mineral exploration – volume: 36 start-page: 2031 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0010 article-title: On the prediction of landslide occurrences and sizes via Hierarchical Neural Networks publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-022-02215-0 – ident: 10.1016/j.earscirev.2024.104927_bb0585 – year: 2013 ident: 10.1016/j.earscirev.2024.104927_bb0340 article-title: Applied logistic regression – ident: 10.1016/j.earscirev.2024.104927_bb0655 – volume: 83 start-page: 249 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0445 article-title: The use of digital technology for rock mass discontinuity mapping: review of benchmarking exercise publication-title: Bull. Eng. Geol. Environ. doi: 10.1007/s10064-024-03730-w – volume: 193 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0095 article-title: The influence of the inventory on the determination of the rainfall–induced shallow landslides susceptibility using generalized additive models publication-title: CATENA doi: 10.1016/j.catena.2020.104630 – volume: 105 start-page: 1348 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0595 article-title: Lasso regression publication-title: Br. J. Surg. doi: 10.1002/bjs.10895 – volume: 37 start-page: 14309 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0470 article-title: A bibliometric analysis of the landslide susceptibility research (1999–2021) publication-title: Geocarto Int. doi: 10.1080/10106049.2022.2087753 – volume: 15 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0760 article-title: From spatio-temporal landslide susceptibility to landslide risk forecast publication-title: Geosci. Front. doi: 10.1016/j.gsf.2023.101765 – volume: 16 start-page: 427 year: 1991 ident: 10.1016/j.earscirev.2024.104927_bb0170 article-title: GIS techniques and statistical models in evaluating landslide hazard publication-title: Earth Surf. Process. Landf. doi: 10.1002/esp.3290160505 – volume: 36 start-page: 2229 year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0495 article-title: From scenario–based seismic hazard to scenario–based landslide hazard: fast–forwarding to the future via statistical simulations publication-title: Stoch. Env. Res. Risk A. doi: 10.1007/s00477-021-02020-1 – volume: 82 start-page: 160 year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0055 article-title: Earthquake–triggered landslide susceptibility in Italy by means of artificial neural network publication-title: Bull. Eng. Geol. Environ. doi: 10.1007/s10064-023-03163-x – start-page: 785 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0185 article-title: XGBoost: a scalable tree boosting system – year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0360 – volume: 323 start-page: 533 year: 1986 ident: 10.1016/j.earscirev.2024.104927_bb0630 article-title: Learning representations by back–propagating errors publication-title: Nature doi: 10.1038/323533a0 – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0015 article-title: Ensemble learning on the benchmark dataset for landslide susceptibility zonation in Central Italy – year: 2023 ident: 10.1016/j.earscirev.2024.104927_bb0485 article-title: Landslide susceptibility within the binomial generalized additive model – volume: 207 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0535 article-title: Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance publication-title: Earth Sci. Rev. doi: 10.1016/j.earscirev.2020.103225 – volume: 9 start-page: 3975 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0040 article-title: Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling publication-title: Geosci. Model Dev. doi: 10.5194/gmd-9-3975-2016 – volume: 7 year: 2020 ident: 10.1016/j.earscirev.2024.104927_bb0675 article-title: Landslide susceptibility evaluation and hazard zonation techniques – a review publication-title: Geoenviron. Disasters doi: 10.1186/s40677-020-00152-0 – volume: 7 start-page: 179 year: 1936 ident: 10.1016/j.earscirev.2024.104927_bb0275 article-title: The use of multiple measurements in taxonomic problems publication-title: Ann. Eugenics doi: 10.1111/j.1469-1809.1936.tb02137.x – volume: 912 year: 2024 ident: 10.1016/j.earscirev.2024.104927_bb0555 article-title: Space–time data–driven modeling of precipitation–induced shallow landslides in South Tyrol, Italy publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2023.169166 – volume: 129 start-page: 376 year: 2011 ident: 10.1016/j.earscirev.2024.104927_bb0285 article-title: Integrating physical and empirical landslide susceptibility models using generalized additive models publication-title: Geomorphology doi: 10.1016/j.geomorph.2011.03.001 – volume: 180 start-page: 60 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0605 article-title: A review of statistically–based landslide susceptibility models publication-title: Earth Sci. Rev. doi: 10.1016/j.earscirev.2018.03.001 – start-page: 5372 year: 2012 ident: 10.1016/j.earscirev.2024.104927_bb0135 article-title: Spatial cross–validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest – volume: vol. 8 year: 1967 ident: 10.1016/j.earscirev.2024.104927_bb0365 article-title: Cybernetics and forecasting techniques – volume: 244 start-page: 14 year: 2018 ident: 10.1016/j.earscirev.2024.104927_bb0490 article-title: Presenting logistic regression–based landslide susceptibility results publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2018.07.019 – volume: 9 start-page: 1 year: 2016 ident: 10.1016/j.earscirev.2024.104927_bb0190 article-title: A GIS–based comparative study of frequency ratio, statistical index and weights–of–evidence models in landslide susceptibility mapping publication-title: Arab. J. Geosci. doi: 10.1007/s12517-015-2150-7 – volume: 78 start-page: 1 year: 1950 ident: 10.1016/j.earscirev.2024.104927_bb0140 article-title: Verification of forecasts expressed in terms of probability publication-title: Mon. Weather Rev. doi: 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2 – volume: 66 start-page: 327 year: 2005 ident: 10.1016/j.earscirev.2024.104927_bb0255 article-title: Artificial neural networks applied to landslide susceptibility assessment publication-title: Geomorphology doi: 10.1016/j.geomorph.2004.09.025 – volume: 123 start-page: 225 year: 2011 ident: 10.1016/j.earscirev.2024.104927_bb0520 article-title: Landslide susceptibility assessment using SVM machine learning algorithm publication-title: Eng. Geol. doi: 10.1016/j.enggeo.2011.09.006 – year: 2022 ident: 10.1016/j.earscirev.2024.104927_bb0415 |
| SSID | ssj0001097 |
| Score | 2.5083048 |
| SecondaryResourceType | review_article |
| Snippet | Landslide susceptibility shows the spatial likelihood of landslide occurrence in a specific geographical area and is a relevant tool for mitigating the impact... |
| SourceID | proquest crossref elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 104927 |
| SubjectTerms | Benchmark dataset data collection Geomorphological mapping Geomorphometry humans Italy Landslide inventory Landslide susceptibility Landslide susceptibility mapping landslides Machine learning quantitative analysis Slope units Spatial analysis Statistical modeling |
| Title | A benchmark dataset and workflow for landslide susceptibility zonation |
| URI | https://dx.doi.org/10.1016/j.earscirev.2024.104927 https://www.proquest.com/docview/3154160846 |
| Volume | 258 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEF6KIngRn1gfZQWvsXlsksZbKdb6KiIWelt2sxus1rS0KVIP_nZnNkmlIvTgKSTsJstkduZj55sZQs6Zo2zGPGaxBItqR5FAEkBkMVdI6WDupcKjgYdu0Omx277fr5BWmQuDtMrC9uc23Vjr4km9kGZ9PBhgjq-DiZeGBQnA2GSwsxC1_OLrh-aBEdbcGsPOh9FLHC9QJng1NntxwVdhvDPC9jJ_e6hftto4oPY22SqQI23mi9shFZ3uko1r05l3vkfaTSph9S_vYvJGkfc51RkVqaLIvEqGow8K8JSazN7hQGk6nU0No8WQY-f0c5QfC-6TXvvqudWxiiYJVgxQIrOU64nIsxNYeRy6IorCSGrXkwAEpdCwPcPYUQAzwBd6MZaS8GNbJr6WSimXNaR3QNbSUaoPCU1sZQvPbugkVkz5QkgnCDDOpuOkIRxRJUEpGB4XFcSxkcWQl1SxV76QKEeJ8lyiVWIvJo7zIhqrp1yWkudL-sDB1K-efFb-Kw67BUMgItWj2ZR7gBidwAbQdfSfDxyTTbzLcxJPyFo2melTACeZrBntq5H1Zuvp_hGvN3ed7je3DubJ |
| linkProvider | Elsevier |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LT8JAEN4QjdGL8RnxuSZeK31sC_VGiIgKnCDhttntbiOKLaElBg_-dmf6wGBMOHhtu-1mdmfm6843M4TcMEuZjDnMYCEW1fZ9gSQA32C2kNLC3EuFRwO9vtcZsqeRO6qQVpkLg7TKwvbnNj2z1sWVWiHN2nQ8xhxfCxMvMxYkAGP4BdpkoL7YxuD264fngSHW3ByD6sPjKyQv2E3wbuz2YoOzwoCnj_1l_nZRv4x15oHae2S3gI60mc9un1R0dEC2HrLWvItD0m5SCdN_eRezN4rEz0SnVESKIvUqnMQfFPApzVJ7J2OlaTJPMkpLxo5d0M84Pxc8IsP2_aDVMYouCUYAWCI1lO0I3zFDmHlQt4Xv132pbUcCEpRCg37WA0sBzgBn6ARYS8INTBm6WiqlbNaQzjHZiOJInxAamsoUjtnQYaCYcoWQludhoE0HYUNYokq8UjA8KEqIYyeLCS-5Yq98KVGOEuW5RKvEXA6c5lU01g-5KyXPVzYEB1u_fvB1uVYc1AVjICLS8TzhDkBGyzMBdZ3-5wNXZLsz6HV597H_fEZ28E6eoHhONtLZXF8AUknlZbYTvwHVf-bC |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+benchmark+dataset+and+workflow+for+landslide+susceptibility+zonation&rft.jtitle=Earth-science+reviews&rft.au=Alvioli%2C+Massimiliano&rft.au=Loche%2C+Marco&rft.au=Jacobs%2C+Liesbet&rft.au=Grohmann%2C+Carlos+H.&rft.date=2024-11-01&rft.pub=Elsevier+B.V&rft.issn=0012-8252&rft.volume=258&rft_id=info:doi/10.1016%2Fj.earscirev.2024.104927&rft.externalDocID=S0012825224002551 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0012-8252&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0012-8252&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0012-8252&client=summon |