Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China
Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine...
Saved in:
| Published in | Hydrological Sciences Journal Vol. 69; no. 11; pp. 1501 - 1522 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Abingdon
Taylor & Francis
17.08.2024
Informa UK Limited Taylor & Francis Ltd |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine learning and deep learning models. Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe efficiency values of 0.976, 0.967, and 0.957, BMA achieved the greatest accuracy for 1-, 2-, and 3-day-ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of BMA in yielding consistently accurate streamflow forecasts. Thus, BMA could provide an efficient alternative approach to multistep-ahead daily streamflow forecasting. The incorporation of data decomposition techniques (e.g. variational mode decomposition) and deep learning algorithms (e.g. deep belief network) into BMA may provide worthy technical references for supervised learning of streamflow systems in data-scarce regions. |
|---|---|
| AbstractList | Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine learning and deep learning models. Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe efficiency values of 0.976, 0.967, and 0.957, BMA achieved the greatest accuracy for 1-, 2-, and 3-day-ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of BMA in yielding consistently accurate streamflow forecasts. Thus, BMA could provide an efficient alternative approach to multistep-ahead daily streamflow forecasting. The incorporation of data decomposition techniques (e.g. variational mode decomposition) and deep learning algorithms (e.g. deep belief network) into BMA may provide worthy technical references for supervised learning of streamflow systems in data-scarce regions. |
| Author | Feng, Qi Barzegar, Rahim Adamowski, Jan F. Wen, Xiaohu Yu, Haijiao Yang, Linshan |
| Author_xml | – sequence: 1 givenname: Haijiao surname: Yu fullname: Yu, Haijiao organization: Linyi University – sequence: 2 givenname: Linshan surname: Yang fullname: Yang, Linshan email: yanglsh08@lzb.ac.cn organization: Qilian Mountains Eco-environment Research Center in Gansu Province – sequence: 3 givenname: Qi surname: Feng fullname: Feng, Qi organization: Qilian Mountains Eco-environment Research Center in Gansu Province – sequence: 4 givenname: Rahim surname: Barzegar fullname: Barzegar, Rahim organization: Université du Québec en Abitibi-Témiscamingue – sequence: 5 givenname: Jan F. surname: Adamowski fullname: Adamowski, Jan F. organization: United Nations University, Institute for Water, Environment and Health – sequence: 6 givenname: Xiaohu surname: Wen fullname: Wen, Xiaohu organization: Qilian Mountains Eco-environment Research Center in Gansu Province |
| BackLink | https://cir.nii.ac.jp/crid/1870303266592437248$$DView record in CiNii |
| BookMark | eNqFkc2OFCEUhYkZE3tGH8GkEl24qZYCCoq40XTGn2QSN7omVHGxmVDQAp1O731wIT0mZha64W6-cw_3nGt0FWIAhF4OeDvgCb_FhBPOudgSTNiWUMEmPj1BGzKMuKeMjldo05i-Qc_Qdc73GFMmOd2gX7chwzp76DzoFFz40UXbGVjieojZFRdDP-sMplv1snfhL26NBnzubEzdevTF5QKHXu9Bm85o589dLgn0an08NQgWnUvTudCFmMr-BLl0u7pTP0dPrfYZXjzMG_T94-233ef-7uunL7sPd_3COC89WaxlgEGOxIABJgUHyWdJODUTmwUGM9NBMm1HI7QcLAhq68kMBjqJydAb9Oay95Diz2O1V6vLC3ivA8RjVnQYqeAjJmNFXz1C7-Mxhfo7RbGUknNJRaXeXaglxZwTWLW4oltoJdUI1IBVa0j9aUi1htRDQ1U9PlIfklt1Ov9X9_qiC85Vw_YOk8AU0wqPkjAqCGvY-wvmQo1_1aeYvFFFn31MNumwuHbxP51-A7ijtmY |
| CitedBy_id | crossref_primary_10_1016_j_apor_2025_104496 crossref_primary_10_1080_02626667_2025_2471430 |
| Cites_doi | 10.1007/s00034-023-02496-y 10.1016/j.jhydrol.2021.126225 10.3390/s23041872 10.1016/j.engappai.2023.106408 10.1038/s41598-019-56847-4 10.1007/s11269-014-0860-3 10.1007/s11269-023-03579-w 10.1016/0005-1098(76)90077-7 10.1016/j.proeng.2016.07.546 10.1016/j.jhydrol.2019.124253 10.1016/j.jhydrol.2023.130438 10.1016/j.jhydrol.2020.125577 10.1016/j.jece.2023.110086 10.1029/2017WR021857 10.1029/2020WR028713 10.1029/2000JD900719 10.1007/s00521-017-3128-z 10.1016/j.envsoft.2020.104669 10.1016/j.agwat.2022.108062 10.1016/j.jhydrol.2021.126196 10.1016/j.ymssp.2023.110913 10.1016/j.est.2023.109196 10.1016/j.jhydrol.2012.11.015 10.1007/BF00927673 10.13031/2013.23153 10.1016/j.jhydrol.2020.124544 10.1016/B978-0-12-820673-7.00008-1 10.1214/aos/1013203451 10.1002/hyp.14276 10.1016/j.jhydrol.2015.06.041 10.1016/j.apgeochem.2012.10.007 10.1007/s11269-023-03688-6 10.1016/S0167-9473(01)00065-2 10.1371/journal.pone.0248489 10.1007/s12206-023-1225-8 10.1007/s13201-023-01943-0 10.1016/j.ecoinf.2023.102452 10.1109/TSP.2013.2288675 10.1029/2021WR030058 10.3390/w15193455 10.3390/w15050947 10.1016/j.jhydrol.2018.05.003 10.2166/wcc.2023.240 10.1007/s11269-023-03722-7 10.1016/j.catena.2019.02.012 10.1002/2017WR020482 10.1007/s00521-020-05523-0 10.1016/j.jhydrol.2011.11.054 10.1016/j.scitotenv.2017.04.189 10.1162/neco.2006.18.7.1527 10.1016/j.accre.2017.08.004 10.1016/j.jhydrol.2019.01.072 10.2166/hydro.2023.116 10.5194/hess-24-2343-2020 10.1016/j.jhydrol.2020.124901 10.1016/j.ymssp.2018.01.019 10.1016/j.ecolind.2023.110092 10.1016/j.knosys.2017.02.013 10.1016/j.jhydrol.2021.126794 10.1038/s41598-021-96751-4 10.1007/s11269-022-03264-4 10.1029/2020WR028392 10.1016/j.engappai.2023.107034 10.1029/2020WR028390 10.1038/s41598-023-50600-8 10.1016/j.ecoinf.2023.102109 10.1029/2022GL102611 10.1016/j.physa.2018.11.061 10.1016/j.eswa.2023.123076 10.1007/s00521-020-04995-4 10.1016/j.jhydrol.2021.126423 10.1007/BF01580138 10.3390/w15213843 10.1016/j.jhydrol.2021.126266 10.1016/j.agwat.2019.03.045 10.1109/ICASSP.2011.5947265 10.1214/ss/1009212519 10.1016/j.scitotenv.2019.01.054 10.1016/j.energy.2019.116502 10.3390/rs16020283 10.1016/j.jhydrol.2021.126258 10.1016/j.jhydrol.2018.12.060 10.2166/nh.2023.024 10.1016/j.atmosres.2020.105167 10.3390/w15203624 10.1029/2020WR028029 10.1007/s40333-019-0092-8 10.1016/j.scitotenv.2020.144715 10.1007/s11269-019-2183-x 10.1016/j.asoc.2021.107081 10.1016/j.geomorph.2019.02.011 10.1016/j.jhydrol.2021.126154 10.1155/2020/4064851 10.5194/hess-24-5491-2020 10.1080/02626667.2022.2149334 10.1016/j.ecoinf.2023.102455 10.1016/j.jhydrol.2023.130320 10.1016/j.jhydrol.2020.124627 10.1080/02626667.2019.1661417 10.1016/j.jher.2023.07.004 |
| ContentType | Journal Article |
| Copyright | 2024 IAHS 2024 2024 IAHS |
| Copyright_xml | – notice: 2024 IAHS 2024 – notice: 2024 IAHS |
| DBID | RYH AAYXX CITATION 7QH 7ST 7TG 7UA C1K F1W H96 KL. L.G SOI 7S9 L.6 |
| DOI | 10.1080/02626667.2024.2374868 |
| DatabaseName | CiNii Complete CrossRef Aqualine Environment Abstracts Meteorological & Geoastrophysical Abstracts Water Resources Abstracts Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources Meteorological & Geoastrophysical Abstracts - Academic Aquatic Science & Fisheries Abstracts (ASFA) Professional Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Aquatic Science & Fisheries Abstracts (ASFA) Professional Meteorological & Geoastrophysical Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources ASFA: Aquatic Sciences and Fisheries Abstracts Aqualine Environment Abstracts Meteorological & Geoastrophysical Abstracts - Academic Water Resources Abstracts Environmental Sciences and Pollution Management AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Aquatic Science & Fisheries Abstracts (ASFA) Professional AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Geography |
| EISSN | 2150-3435 |
| EndPage | 1522 |
| ExternalDocumentID | 10_1080_02626667_2024_2374868 2374868 |
| Genre | Research Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: the Young Elite Scientist Sponsorship Program of CAST grantid: YESS20200089 – fundername: the Strategic Priority Research Program of CAS grantid: XDB0720202 – fundername: the Youth Innovation Promotion Association of CAS grantid: 2022435 – fundername: National Natural Science Fund of China grantid: 52379030 |
| GroupedDBID | .7F .QJ 0BK 0R~ 30N 4.4 5GY AAENE AAHBH AAJMT AALDU AAMIU AAPUL AAQRR ABCCY ABDBF ABFIM ABHAV ABJNI ABLIJ ABPAQ ABPEM ABTAI ABXUL ABXYU ACGFS ACTIO ADCVX ADGTB AEISY AEOZL AEPSL AEYOC AFRAH AGDLA AGMYJ AHDZW AIJEM AKBVH AKOOK ALMA_UNASSIGNED_HOLDINGS ALQZU AQRUH AVBZW AWYRJ BLEHA CCCUG CE4 CS3 DGEBU DKSSO EBS ESX E~A E~B FRP GBTJF GTTXZ H13 HF~ HZ~ H~P IPNFZ J.P KYCEM LJTGL M4Z NA5 NX0 O9- P2P REX RIG RNANH ROSJB RTWRZ S-T SNACF TBQAZ TDBHL TEI TFL TFT TFW TQWBC TTHFI TUROJ UT5 UU3 XJN ZGOLN ~02 ~S~ AAGDL AAHIA ADYSH AFRVT AIYEW AMPGV RYH AAYXX CITATION 7QH 7ST 7TG 7UA ACUHS C1K F1W H96 KL. L.G SOI TASJS 7S9 L.6 |
| ID | FETCH-LOGICAL-c466t-2cff4e0e952dede4976e96b9263d84b70edb3194af5d7a91fe73f6674e13878d3 |
| ISSN | 0262-6667 2150-3435 |
| IngestDate | Fri Jul 11 09:17:42 EDT 2025 Wed Aug 13 04:32:52 EDT 2025 Tue Jul 01 04:12:49 EDT 2025 Thu Apr 24 22:55:51 EDT 2025 Thu Jun 26 23:00:21 EDT 2025 Wed Dec 25 09:06:47 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c466t-2cff4e0e952dede4976e96b9263d84b70edb3194af5d7a91fe73f6674e13878d3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| PQID | 3099966937 |
| PQPubID | 436427 |
| PageCount | 22 |
| ParticipantIDs | nii_cinii_1870303266592437248 crossref_citationtrail_10_1080_02626667_2024_2374868 proquest_journals_3099966937 informaworld_taylorfrancis_310_1080_02626667_2024_2374868 proquest_miscellaneous_3153765025 crossref_primary_10_1080_02626667_2024_2374868 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-17 |
| PublicationDateYYYYMMDD | 2024-08-17 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-17 day: 17 |
| PublicationDecade | 2020 |
| PublicationPlace | Abingdon |
| PublicationPlace_xml | – name: Abingdon |
| PublicationTitle | Hydrological Sciences Journal |
| PublicationYear | 2024 |
| Publisher | Taylor & Francis Informa UK Limited Taylor & Francis Ltd |
| Publisher_xml | – name: Taylor & Francis – name: Informa UK Limited – name: Taylor & Francis Ltd |
| References | e_1_3_5_27_1 e_1_3_5_100_1 e_1_3_5_23_1 e_1_3_5_46_1 e_1_3_5_69_1 e_1_3_5_88_1 e_1_3_5_108_1 e_1_3_5_61_1 e_1_3_5_80_1 e_1_3_5_42_1 e_1_3_5_65_1 e_1_3_5_84_1 e_1_3_5_9_1 e_1_3_5_5_1 Hinton G. (e_1_3_5_40_1) 2010 e_1_3_5_39_1 e_1_3_5_16_1 e_1_3_5_35_1 e_1_3_5_12_1 e_1_3_5_77_1 e_1_3_5_58_1 e_1_3_5_50_1 e_1_3_5_92_1 e_1_3_5_73_1 e_1_3_5_54_1 e_1_3_5_96_1 e_1_3_5_31_1 e_1_3_5_101_1 e_1_3_5_28_1 e_1_3_5_24_1 e_1_3_5_109_1 e_1_3_5_66_1 e_1_3_5_47_1 e_1_3_5_89_1 e_1_3_5_105_1 e_1_3_5_81_1 e_1_3_5_62_1 e_1_3_5_43_1 e_1_3_5_85_1 e_1_3_5_8_1 e_1_3_5_20_1 e_1_3_5_4_1 e_1_3_5_17_1 e_1_3_5_13_1 e_1_3_5_36_1 e_1_3_5_55_1 e_1_3_5_78_1 e_1_3_5_59_1 e_1_3_5_70_1 e_1_3_5_93_1 e_1_3_5_51_1 e_1_3_5_74_1 e_1_3_5_97_1 e_1_3_5_32_1 e_1_3_5_29_1 e_1_3_5_102_1 e_1_3_5_25_1 Hu H. (e_1_3_5_44_1) 2020; 2020 e_1_3_5_67_1 e_1_3_5_48_1 e_1_3_5_106_1 e_1_3_5_82_1 e_1_3_5_3_1 e_1_3_5_63_1 e_1_3_5_86_1 e_1_3_5_21_1 e_1_3_5_7_1 e_1_3_5_18_1 e_1_3_5_37_1 e_1_3_5_14_1 e_1_3_5_33_1 e_1_3_5_56_1 Shi Y. (e_1_3_5_79_1) 2005 e_1_3_5_94_1 e_1_3_5_71_1 e_1_3_5_52_1 e_1_3_5_98_1 e_1_3_5_75_1 e_1_3_5_10_1 e_1_3_5_90_1 e_1_3_5_26_1 e_1_3_5_22_1 e_1_3_5_45_1 e_1_3_5_107_1 e_1_3_5_68_1 e_1_3_5_49_1 e_1_3_5_103_1 e_1_3_5_83_1 e_1_3_5_2_1 e_1_3_5_60_1 e_1_3_5_41_1 e_1_3_5_87_1 e_1_3_5_64_1 Zhang X. (e_1_3_5_104_1) 2001; 43 e_1_3_5_6_1 e_1_3_5_38_1 e_1_3_5_15_1 e_1_3_5_11_1 e_1_3_5_34_1 e_1_3_5_57_1 e_1_3_5_99_1 e_1_3_5_19_1 e_1_3_5_72_1 e_1_3_5_91_1 e_1_3_5_53_1 e_1_3_5_76_1 e_1_3_5_95_1 e_1_3_5_30_1 |
| References_xml | – ident: e_1_3_5_3_1 doi: 10.1007/s00034-023-02496-y – ident: e_1_3_5_57_1 doi: 10.1016/j.jhydrol.2021.126225 – ident: e_1_3_5_49_1 doi: 10.3390/s23041872 – ident: e_1_3_5_96_1 doi: 10.1016/j.engappai.2023.106408 – ident: e_1_3_5_12_1 doi: 10.1038/s41598-019-56847-4 – ident: e_1_3_5_32_1 – ident: e_1_3_5_26_1 doi: 10.1007/s11269-014-0860-3 – ident: e_1_3_5_85_1 doi: 10.1007/s11269-023-03579-w – ident: e_1_3_5_7_1 doi: 10.1016/0005-1098(76)90077-7 – ident: e_1_3_5_63_1 doi: 10.1016/j.proeng.2016.07.546 – ident: e_1_3_5_14_1 doi: 10.1016/j.jhydrol.2019.124253 – ident: e_1_3_5_93_1 doi: 10.1016/j.jhydrol.2023.130438 – ident: e_1_3_5_15_1 doi: 10.1016/j.jhydrol.2020.125577 – ident: e_1_3_5_90_1 doi: 10.1016/j.jece.2023.110086 – ident: e_1_3_5_62_1 doi: 10.1029/2017WR021857 – ident: e_1_3_5_75_1 doi: 10.1029/2020WR028713 – ident: e_1_3_5_82_1 doi: 10.1029/2000JD900719 – ident: e_1_3_5_81_1 doi: 10.1007/s00521-017-3128-z – ident: e_1_3_5_86_1 doi: 10.1016/j.envsoft.2020.104669 – ident: e_1_3_5_92_1 doi: 10.1016/j.agwat.2022.108062 – ident: e_1_3_5_6_1 doi: 10.1016/j.jhydrol.2021.126196 – ident: e_1_3_5_87_1 doi: 10.1016/j.ymssp.2023.110913 – ident: e_1_3_5_51_1 doi: 10.1016/j.est.2023.109196 – ident: e_1_3_5_22_1 doi: 10.1016/j.jhydrol.2012.11.015 – ident: e_1_3_5_39_1 doi: 10.1007/BF00927673 – ident: e_1_3_5_60_1 doi: 10.13031/2013.23153 – ident: e_1_3_5_53_1 doi: 10.1016/j.jhydrol.2020.124544 – ident: e_1_3_5_23_1 doi: 10.1016/B978-0-12-820673-7.00008-1 – ident: e_1_3_5_28_1 doi: 10.1214/aos/1013203451 – ident: e_1_3_5_107_1 doi: 10.1002/hyp.14276 – ident: e_1_3_5_102_1 doi: 10.1016/j.jhydrol.2015.06.041 – ident: e_1_3_5_58_1 doi: 10.1016/j.apgeochem.2012.10.007 – ident: e_1_3_5_4_1 doi: 10.1007/s11269-023-03688-6 – ident: e_1_3_5_29_1 doi: 10.1016/S0167-9473(01)00065-2 – ident: e_1_3_5_97_1 doi: 10.1371/journal.pone.0248489 – ident: e_1_3_5_48_1 doi: 10.1007/s12206-023-1225-8 – ident: e_1_3_5_2_1 doi: 10.1007/s13201-023-01943-0 – ident: e_1_3_5_35_1 – ident: e_1_3_5_16_1 doi: 10.1016/j.ecoinf.2023.102452 – volume-title: Concise glacier inventory of China year: 2005 ident: e_1_3_5_79_1 – ident: e_1_3_5_19_1 doi: 10.1109/TSP.2013.2288675 – ident: e_1_3_5_20_1 – ident: e_1_3_5_76_1 doi: 10.1029/2021WR030058 – ident: e_1_3_5_54_1 doi: 10.3390/w15193455 – ident: e_1_3_5_67_1 doi: 10.3390/w15050947 – ident: e_1_3_5_71_1 doi: 10.1016/j.jhydrol.2018.05.003 – ident: e_1_3_5_80_1 doi: 10.2166/wcc.2023.240 – ident: e_1_3_5_59_1 doi: 10.1007/s11269-023-03722-7 – ident: e_1_3_5_70_1 doi: 10.1016/j.catena.2019.02.012 – ident: e_1_3_5_94_1 doi: 10.1002/2017WR020482 – ident: e_1_3_5_31_1 doi: 10.1007/s00521-020-05523-0 – ident: e_1_3_5_45_1 doi: 10.1016/j.jhydrol.2011.11.054 – ident: e_1_3_5_5_1 doi: 10.1016/j.scitotenv.2017.04.189 – ident: e_1_3_5_41_1 doi: 10.1162/neco.2006.18.7.1527 – ident: e_1_3_5_88_1 doi: 10.1016/j.accre.2017.08.004 – ident: e_1_3_5_43_1 doi: 10.1016/j.jhydrol.2019.01.072 – ident: e_1_3_5_21_1 doi: 10.2166/hydro.2023.116 – ident: e_1_3_5_56_1 doi: 10.5194/hess-24-2343-2020 – ident: e_1_3_5_65_1 doi: 10.1016/j.jhydrol.2020.124901 – ident: e_1_3_5_55_1 doi: 10.1016/j.ymssp.2018.01.019 – ident: e_1_3_5_13_1 doi: 10.1016/j.ecolind.2023.110092 – volume-title: A practical guide to training restricted boltzmann machines (Version 1) year: 2010 ident: e_1_3_5_40_1 – ident: e_1_3_5_17_1 doi: 10.1016/j.knosys.2017.02.013 – ident: e_1_3_5_36_1 doi: 10.1016/j.jhydrol.2021.126794 – ident: e_1_3_5_33_1 doi: 10.1038/s41598-021-96751-4 – ident: e_1_3_5_89_1 doi: 10.1007/s11269-022-03264-4 – ident: e_1_3_5_84_1 doi: 10.1029/2020WR028392 – ident: e_1_3_5_78_1 doi: 10.1016/j.engappai.2023.107034 – ident: e_1_3_5_69_1 doi: 10.1029/2020WR028390 – ident: e_1_3_5_106_1 doi: 10.1038/s41598-023-50600-8 – ident: e_1_3_5_9_1 doi: 10.1016/j.ecoinf.2023.102109 – ident: e_1_3_5_99_1 doi: 10.1029/2022GL102611 – ident: e_1_3_5_11_1 doi: 10.1016/j.physa.2018.11.061 – ident: e_1_3_5_47_1 doi: 10.1016/j.eswa.2023.123076 – ident: e_1_3_5_10_1 doi: 10.1007/s00521-020-04995-4 – ident: e_1_3_5_52_1 doi: 10.1016/j.jhydrol.2021.126423 – ident: e_1_3_5_73_1 doi: 10.1007/BF01580138 – ident: e_1_3_5_24_1 doi: 10.3390/w15213843 – ident: e_1_3_5_108_1 doi: 10.1016/j.jhydrol.2021.126266 – ident: e_1_3_5_61_1 doi: 10.1016/j.agwat.2019.03.045 – ident: e_1_3_5_83_1 doi: 10.1109/ICASSP.2011.5947265 – ident: e_1_3_5_25_1 – ident: e_1_3_5_42_1 doi: 10.1214/ss/1009212519 – ident: e_1_3_5_68_1 doi: 10.1016/j.scitotenv.2019.01.054 – ident: e_1_3_5_98_1 doi: 10.1016/j.energy.2019.116502 – ident: e_1_3_5_103_1 doi: 10.3390/rs16020283 – ident: e_1_3_5_34_1 doi: 10.1016/j.jhydrol.2021.126258 – ident: e_1_3_5_91_1 doi: 10.1016/j.jhydrol.2018.12.060 – ident: e_1_3_5_38_1 doi: 10.2166/nh.2023.024 – ident: e_1_3_5_30_1 – ident: e_1_3_5_100_1 doi: 10.1016/j.atmosres.2020.105167 – ident: e_1_3_5_74_1 doi: 10.3390/w15203624 – ident: e_1_3_5_8_1 doi: 10.1029/2020WR028029 – ident: e_1_3_5_105_1 doi: 10.1007/s40333-019-0092-8 – ident: e_1_3_5_95_1 doi: 10.1016/j.scitotenv.2020.144715 – ident: e_1_3_5_37_1 doi: 10.1007/s11269-019-2183-x – ident: e_1_3_5_77_1 doi: 10.1016/j.asoc.2021.107081 – volume: 43 start-page: 1294 issue: 12 year: 2001 ident: e_1_3_5_104_1 article-title: Ecological restoration and sustainable agricultural paradigm of mountain-oasis-ecotone-desert system in the north of the Tianshan Mountains publication-title: Journal of Integrative Plant Biology – ident: e_1_3_5_66_1 doi: 10.1016/j.geomorph.2019.02.011 – ident: e_1_3_5_101_1 doi: 10.1016/j.jhydrol.2021.126154 – volume: 2020 start-page: 1 year: 2020 ident: e_1_3_5_44_1 article-title: A comparative study of VMD-based hybrid forecasting model for nonstationary daily streamflow time series publication-title: Complexity doi: 10.1155/2020/4064851 – ident: e_1_3_5_109_1 doi: 10.5194/hess-24-5491-2020 – ident: e_1_3_5_18_1 doi: 10.1080/02626667.2022.2149334 – ident: e_1_3_5_46_1 doi: 10.1016/j.ecoinf.2023.102455 – ident: e_1_3_5_64_1 doi: 10.1016/j.jhydrol.2023.130320 – ident: e_1_3_5_27_1 doi: 10.1016/j.jhydrol.2020.124627 – ident: e_1_3_5_72_1 doi: 10.1080/02626667.2019.1661417 – ident: e_1_3_5_50_1 doi: 10.1016/j.jher.2023.07.004 |
| SSID | ssj0034963 ssib006544270 ssib031649816 ssib002496053 ssib001220490 ssib001134578 ssib001711759 ssib006544269 ssib020738279 |
| Score | 2.4104457 |
| Snippet | Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast... |
| SourceID | proquest crossref nii informaworld |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1501 |
| SubjectTerms | Algorithms Arid regions Arid zones Bayesian analysis Bayesian model averaging Bayesian theory Belief networks China Daily Daily forecasts Decomposition Deep learning Ensemble learning Forecasting Learning algorithms Machine learning Mathematical models oases oasis Probability theory Stream discharge Stream flow Streamflow forecasting Supervised learning Uncertainty analysis |
| Title | Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China |
| URI | https://www.tandfonline.com/doi/abs/10.1080/02626667.2024.2374868 https://cir.nii.ac.jp/crid/1870303266592437248 https://www.proquest.com/docview/3099966937 https://www.proquest.com/docview/3153765025 |
| Volume | 69 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Ni9NAFB_qetCL-InVXRnBW0lJJpOvo0jXIuuK0EJvYZKZuJE2LW2KuGev_s--N5lJUrewq5dQJjPJNO-X95X3Qcg7wTIeJFI6LFGuw5mSTgxSwolYHsQsgVUBJid_vgync_5pESwGg9-9qKV9nY3z66N5Jf9DVRgDumKW7D9Qtr0oDMBvoC8cgcJwvBONJ9VOrTD1aWkdHKD6SYVh4iYWy0EpJUcrHTLZm6cb4OhSDE1IIdB64whgzHIkBXo8MIdErIrl-gdOUrnY1Sb7pdIN2EGYdL23rXY7_Sm3LTM1snU36v8V3QJMiztRfi_Fuh00bmswjXdXHWDhaevhr2Xncd1eq28mKlxclau-24Jx9MM2WZoNdwNVAwQAb2qVWFbcdG2xkPNGmzHM8hzQMViPyeJYT2DbszeEgYmeZGCzhWE0xl2MGZbbaRr5HBbfvvySns8vLtLZZDG7R-4zsDqQbfrupRXsWFofAxbanduEMCzVfuwmB6rOQSFcUGSqsrwh_rVOM3tMHhljhL5vkPWEDFT1lDz4qEwZ82fkl0UYtcih64IeQRg1COvmNQijsBv6F8KoRhjtEEZ7CKNlRVuEUY2w52R-Ppl9mDqmb4eT8zCsHZYXBVeuSgImlVQcNF6VhFnCQl_GPItcJTPg_FwUgYxE4hUq8gt4eFx5fhzF0n9BTqp1pV4Smrsiy8GE8WPUJb0M1NEsi5h0VcZU7Ikh4fYRp7kpao-9VZapZ2vfGsqkSJnUUGZIxu2yTVPV5bYFSZ9-aa3daUXT-yb1b1l7BsSG7eHRi1HEgs2E4Qz4zZzD-VMLg9S8kXBJV3siwHgYkrftaWD9-D1PVGq9x9tiLaYArJZXd5jzmjzs3sNTclJv9-oMFOo6e6Nh_geURseb |
| linkProvider | Taylor & Francis |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELZKOZQLb8T2AUaCo5fEcV4HDghabWm7p1bqLdjxGFbsZis2q2o584v4K_whZhynakGoB9QDl1wcJ5Znxt-MPf6GsZdaGpWW1gpZQiSUBCsKRAmRyzotZIm9UrqcfDTORifqw2l6usZ-9HdhKK2SYmjXEUX4tZqMmzaj-5S41xg3IK5kOYZ3Ug0lMahkRUisPIDVOYZtizf771HGr6Tc2z1-NxKhsoCoVZa1QtbOKYigTKUFCwoxGcrMlDJLbKFMHoE1qJtKu9Tmuowd5InDHyqIkyIvbILfvcVup2WWk20l0bhf_Yl_Pen2daSgMfa3hv427Ct4eIUtFdGumUz-wAgPfHv32M9-yrp8ly_DZWuG9bff2CT_rzm9z-4GP5y_7QznAVuD5iHbCCXhP68ese-7zQJmZgo8FNb4xOeOW6Ac_JDoJsgFsHzm81EvveerCy04zjH3-ZpoSGdCI-pZbvVkuuJ0QUfP3HR-Ti9BrReUfM4nDW98dXuUAveFzR-zkxuZhCdsvZk38JTxOtKmRu89KciNig16Ysbk0kZgJBSxHjDVK05VBz53KisyreKe9jWItCKRVkGkAza86HbWEZpc16G8rJVV63eSXFf2pUqu6buDKozDo2dcELpguEAn-XRcrLB9u1fuKqyd-MnIB-HoNw_Yi4tmXPXoKEs3MF_Sb4mGKEWHffMfhvecbYyOjw6rw_3xwRa7Q010XBDn22y9_bqEHfQ3W_PMGzhnH29a-38BO9iAvw |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbtQwELZKkYAL_4iFFowERy-J4_wdOCDaVUthxYFKvQU7HpcVu9kVm1W1nHkhXoUnYsZxqhaEekA9cMnFcWJ5xp4Z-5tvGHuupVFpaa2QJURCSbCiQCshclmnhSyxV0rJye_H2d6henuUHm2wH30uDMEqKYZ2HVGE36tpcS-s6xFxLzFsQLOS5RjdSTWURKCSFQFXeQDrE4zalq_2d1DEL6Qc7X58sydCYQFRqyxrhaydUxBBmUoLFhSaZCgzU8ossYUyeQTWoGoq7VKb6zJ2kCcOf6ggToq8sAl-9wq7mlFiJ2WNRON-8yf69aQ71pGCxtgnDf1t2OfM4TmyVDR2zWTyh4nwdm90i_3sZ6yDu3wZrlozrL_9Rib5X03pbXYzeOH8dbds7rANaO6y66Eg_Of1PfZ9t1nCzEyBh7Iax3zuuAVC4AeYmyAHwPKZR6Oeec_XFlpynGLu0Zq4jBZCo82z3OrJdM0pPUfP3HR-Qi9BrZcEPeeThje-tj0Kgfuy5vfZ4aVMwgO22cwbeMh4HWlTo--eFORExQb9MGNyaSMwEopYD5jq9aaqA5s7FRWZVnFP-hpEWpFIqyDSARuedlt0dCYXdSjPKmXV-nMk1xV9qZIL-m6jBuPw6BkXZFswWKB7fLosVti-1et2FXZO_GTkQ3D0mgfs2Wkz7nl0kaUbmK_ot0RClKK7_ugfhveUXfuwM6re7Y8PHrMb1EJ3BXG-xTbbryvYRmezNU_88ubs02Ur_y_ki39j |
| 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=Ensemble+learning+of+decomposition-based+machine+learning+models+for+multistep-ahead+daily+streamflow+forecasting+in+northwest+China&rft.jtitle=Hydrological+sciences+journal&rft.au=Yu%2C+Haijiao&rft.au=Yang%2C+Linshan&rft.au=Feng%2C+Qi&rft.au=Barzegar%2C+Rahim&rft.date=2024-08-17&rft.issn=2150-3435&rft.volume=69&rft.issue=11+p.1501-1522&rft.spage=1501&rft.epage=1522&rft_id=info:doi/10.1080%2F02626667.2024.2374868&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0262-6667&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0262-6667&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0262-6667&client=summon |