Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity
Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer funct...
Saved in:
Published in | Soil & tillage research Vol. 90; no. 1; pp. 108 - 116 |
---|---|
Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Amsterdam
Elsevier B.V
01.11.2006
Elsevier |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (
p
>
0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (
R
2) and the root mean square error (RMSE) between the measured and predicted parameter values. The
R
2 and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies. |
---|---|
AbstractList | Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity curves. As an alternative to direct measurement, indirect determination of these functions from basic soil properties using pedotransfer functions (PTFs) has attracted the attention of researchers in a variety of fields such as soil scientists, hydrologists, and agricultural and environmental engineers. In this study, PTFs for point and parametric (van Genuchten's parameters) estimation of soil hydraulic parameters from basic soil properties such as particle-size distribution, bulk density, and three different pore sizes were developed and validated using artificial neural network (ANN) and multiple-linear regression methods and the predictive capabilities of the two methods was compared using some evaluation criteria. Total of 195 soil samples was divided into two groups as 130 for the development and 65 for the validation of PTFs. Although the differences between the two methods were not statistically significant (
p
>
0.05), regression predicted point and parametric variables of soil hydraulic parameters better than ANN. Both methods had lower accuracy in parametric predictions than in point predictions. Accuracy of the predictions was evaluated by the coefficient of determination (
R
2) and the root mean square error (RMSE) between the measured and predicted parameter values. The
R
2 and RMSE varied from 0.637 to 0.979 and from 0.013 to 0.938 for regression, and varied from 0.444 to 0.952 and from 0.020 to 3.511 for ANN, respectively. Even though regression performs insignificantly better than ANN in this case, ANN produces promising results and its advantages can be utilized by developing or using new algorithms in future studies. |
Author | Merdun, Hasan Çınar, Özer Apan, Mehmet Meral, Ramazan |
Author_xml | – sequence: 1 givenname: Hasan surname: Merdun fullname: Merdun, Hasan email: merdun@alumni.clemson.edu organization: Department of Agricultural Engineering, Faculty of Agriculture, Kahramanmaraş Sütçü İmam University, Kahramanmaraş 46000, Turkey – sequence: 2 givenname: Özer surname: Çınar fullname: Çınar, Özer organization: Department of Environmental Engineering, Faculty of Engineering, Kahramanmaraş Sütçü İmam University, Kahramanmaraş 46000, Turkey – sequence: 3 givenname: Ramazan surname: Meral fullname: Meral, Ramazan organization: Department of Agricultural Engineering, Faculty of Agriculture, Kahramanmaraş Sütçü İmam University, Kahramanmaraş 46000, Turkey – sequence: 4 givenname: Mehmet surname: Apan fullname: Apan, Mehmet organization: Department of Agricultural Engineering, Faculty of Agriculture, Ondokuz Mayıs University, Samsun 55139, Turkey |
BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18110918$$DView record in Pascal Francis |
BookMark | eNp9kMtu3CAUhlGVSJ1cniCLsunS7sHYY7zoohr1JkXqos0aYThOmTpgHZhE8yh92-KZSt11dQR8_w98V-wixICM3QmoBYjtu32dsp_nugHoalA1CPGKbYTqh0q2bXvBNoXqKzGo_jW7SmkPAK1s1Ib93sWnxZBPMfA4cUPZT956M_OABzqN_BLpFzfBccJHwpR8YRd0MZMJaULi0yHYXHYTnyLxhdD503ptTNHP_MXkghFmDKf9tSyZXC7I6PjPoyNzmL3lNgZ3KNFnn4837HIyc8Lbv_OaPXz6-GP3pbr_9vnr7sN9ZWXX50opJRHdiCg717a27TswI26HDhrhtk4qOVhrRzeIphzCaLdGOSNGqxqpRiWvmTz3WoopEU56If9k6KgF6NWu3uuTXb3a1aB0sVtSb8-pxSRr5qmosD79iyohYBBr-5szN5mozWMRrR--NyAkQC-HghTi_ZnA8slnj6ST9RhskUhos3bR__clfwAQRaJq |
CitedBy_id | crossref_primary_10_1007_s12205_015_0210_x crossref_primary_10_1016_j_geodrs_2023_e00723 crossref_primary_10_1016_j_measurement_2016_10_010 crossref_primary_10_1016_j_still_2019_104449 crossref_primary_10_1007_s40009_015_0358_4 crossref_primary_10_1016_j_ecoinf_2022_101959 crossref_primary_10_1016_j_still_2021_104973 crossref_primary_10_1016_j_still_2018_03_021 crossref_primary_10_1080_00103624_2021_1908321 crossref_primary_10_1007_s12665_021_09518_5 crossref_primary_10_2136_vzj2019_06_0063 crossref_primary_10_3390_agriengineering5010004 crossref_primary_10_3390_w13243615 crossref_primary_10_1016_j_compag_2017_08_012 crossref_primary_10_1071_SR18352 crossref_primary_10_1038_s41598_022_06249_w crossref_primary_10_1016_j_coldregions_2012_12_005 crossref_primary_10_1016_j_jhydrol_2024_131302 crossref_primary_10_1016_j_geodrs_2020_e00344 crossref_primary_10_1016_j_agwat_2009_10_011 crossref_primary_10_1007_s12145_022_00909_1 crossref_primary_10_3390_app13010465 crossref_primary_10_1016_j_jhydrol_2014_12_050 crossref_primary_10_1029_2021WR031059 crossref_primary_10_1007_s12145_022_00920_6 crossref_primary_10_2166_nh_2015_219 crossref_primary_10_1002_hyp_10006 crossref_primary_10_1016_j_geoderma_2019_114098 crossref_primary_10_1080_00103624_2019_1648658 crossref_primary_10_1016_j_jhydrol_2021_127171 crossref_primary_10_1016_j_apsoil_2020_103514 crossref_primary_10_1155_2015_535216 crossref_primary_10_2136_vzj2018_08_0151 crossref_primary_10_1016_j_ejrs_2015_06_004 crossref_primary_10_1007_s42452_020_03974_7 crossref_primary_10_1007_s10712_013_9249_8 crossref_primary_10_1016_S1002_0160_11_60122_7 crossref_primary_10_1590_18069657rbcs20170250 crossref_primary_10_1016_j_agwat_2020_106121 crossref_primary_10_5424_sjar_2015131_6111 crossref_primary_10_3390_agronomy11081581 crossref_primary_10_1016_j_jhydrol_2009_03_005 crossref_primary_10_1016_j_biosystemseng_2016_10_013 crossref_primary_10_1556_agrokem_59_2010_1_3 crossref_primary_10_1016_j_jhydrol_2019_05_050 crossref_primary_10_1155_2017_7689415 crossref_primary_10_1007_s40808_016_0255_y crossref_primary_10_3390_w13050705 crossref_primary_10_1007_s11356_020_07868_4 crossref_primary_10_1007_s40808_016_0216_5 crossref_primary_10_1061__ASCE_IR_1943_4774_0001094 crossref_primary_10_1590_S0100_204X2010000500009 crossref_primary_10_1016_j_compag_2020_105502 crossref_primary_10_1016_j_clay_2015_07_035 crossref_primary_10_1007_s11368_018_2040_1 crossref_primary_10_1016_j_geodrs_2020_e00289 crossref_primary_10_1007_s00500_019_03847_1 crossref_primary_10_1007_s10333_021_00886_z crossref_primary_10_1016_j_compag_2022_106862 crossref_primary_10_1016_j_ecolind_2021_107382 crossref_primary_10_1002_jpln_201300176 crossref_primary_10_1007_s11368_018_2036_x crossref_primary_10_1016_j_still_2023_105750 crossref_primary_10_1111_ejss_12959 crossref_primary_10_1016_j_geoderma_2022_115864 crossref_primary_10_1016_j_gsd_2022_100778 crossref_primary_10_1155_2013_308159 crossref_primary_10_1016_j_catena_2020_104467 crossref_primary_10_3724_SP_J_1011_2012_01096 crossref_primary_10_1016_j_geoderma_2019_07_031 crossref_primary_10_1371_journal_pone_0296933 crossref_primary_10_1155_2014_740521 crossref_primary_10_1007_s41324_022_00452_7 crossref_primary_10_1016_j_ejrh_2021_100832 crossref_primary_10_1007_s11269_014_0553_y crossref_primary_10_1002_ird_2566 crossref_primary_10_1007_s13762_022_03980_9 crossref_primary_10_1007_s42729_021_00756_x crossref_primary_10_1007_s11600_019_00283_5 crossref_primary_10_1016_j_still_2017_04_009 crossref_primary_10_3390_agronomy10060823 crossref_primary_10_1016_j_agwat_2018_12_005 crossref_primary_10_3390_w10101431 crossref_primary_10_1016_j_catena_2020_104479 crossref_primary_10_1071_SR13230 crossref_primary_10_1080_00103624_2020_1729374 crossref_primary_10_1080_03650340_2015_1109078 crossref_primary_10_3390_atmos14111644 crossref_primary_10_3390_land10090959 crossref_primary_10_3390_agriculture14010047 crossref_primary_10_1007_s00521_010_0425_1 crossref_primary_10_2136_vzj2018_07_0141 crossref_primary_10_1080_00103624_2020_1822385 crossref_primary_10_2136_vzj2010_0140 crossref_primary_10_1080_10106049_2022_2076918 crossref_primary_10_1080_09715010_2017_1400408 crossref_primary_10_1007_s00704_012_0769_9 crossref_primary_10_1097_SS_0b013e3182316c93 crossref_primary_10_1002_ird_2584 crossref_primary_10_1016_j_scitotenv_2017_01_020 crossref_primary_10_1080_03650340_2020_1808626 crossref_primary_10_1016_j_apsoil_2017_02_011 crossref_primary_10_1016_S1002_0160_15_60049_2 crossref_primary_10_1080_03650340_2010_512289 crossref_primary_10_3390_agronomy12071507 crossref_primary_10_1016_j_catena_2019_104315 crossref_primary_10_1097_SS_0000000000000253 crossref_primary_10_1007_s12517_022_10565_w crossref_primary_10_1061__ASCE_HE_1943_5584_0000824 crossref_primary_10_1016_j_proeng_2012_04_200 crossref_primary_10_1016_j_compag_2023_108118 crossref_primary_10_1061__ASCE_HE_1943_5584_0000666 crossref_primary_10_1002_hyp_10173 crossref_primary_10_1016_j_envsoft_2013_09_022 |
Cites_doi | 10.2136/sssaj1999.634955x 10.2136/sssaj1995.03615995005900030034x 10.2136/sssaj1998.03615995006200040001x 10.2136/sssaj2002.0352 10.1016/S0167-1987(98)00070-1 10.1097/00010694-198912000-00002 10.1046/j.1365-2389.1999.00247.x 10.1016/S0016-7061(99)00061-0 10.2136/sssaj2000.641327x 10.2136/sssaj1988.03615995005200050003x 10.1016/S0022-1694(01)00464-4 10.2136/sssaj1996.03615995006000030007x 10.2136/sssaj1999.6361748x 10.1046/j.1365-2389.2003.00485.x 10.2136/sssaj2004.0417 10.1097/00010694-198412000-00001 10.1016/0016-7061(95)00089-5 10.1016/0016-7061(94)00079-P 10.2136/sssaj1980.03615995004400050002x 10.1007/978-1-4612-3532-3_4 10.1016/0016-7061(96)00044-4 10.1023/A:1022935810223 10.2136/sssaj1996.03615995006000060018x 10.1016/S0016-7061(98)00129-3 |
ContentType | Journal Article |
Copyright | 2005 Elsevier B.V. 2007 INIST-CNRS |
Copyright_xml | – notice: 2005 Elsevier B.V. – notice: 2007 INIST-CNRS |
DBID | FBQ IQODW AAYXX CITATION |
DOI | 10.1016/j.still.2005.08.011 |
DatabaseName | AGRIS Pascal-Francis CrossRef |
DatabaseTitle | CrossRef |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: FBQ name: AGRIS url: http://www.fao.org/agris/Centre.asp?Menu_1ID=DB&Menu_2ID=DB1&Language=EN&Content=http://www.fao.org/agris/search?Language=EN sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Agriculture |
EISSN | 1879-3444 |
EndPage | 116 |
ExternalDocumentID | 10_1016_j_still_2005_08_011 18110918 US201300739091 S0167198705002436 |
GroupedDBID | --K --M .~1 0R~ 123 1B1 1RT 1~. 1~5 4.4 457 4G. 5VS 7-5 71M 8P~ 9JM 9JN AABVA AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALCJ AALRI AAOAW AAQFI AAQXK AARJD AATLK AAXUO ABFNM ABFRF ABGRD ABJNI ABMAC ABXDB ABYKQ ACDAQ ACGFO ACGFS ACIUM ACNNM ACRLP ADBBV ADEZE ADMUD ADQTV ADTZH AEBSH AECPX AEFWE AEKER AENEX AEQOU AFKWA AFTJW AFXIZ AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BJAXD BKOJK BLXMC CBWCG CS3 DU5 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HLV HMC HVGLF HZ~ IHE J1W JARJE JJJVA KOM LW9 LY9 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAB SDF SDG SEN SES SEW SPC SPCBC SSA SSR SST SSZ T5K TWZ UNMZH WUQ Y6R ~02 ~G- ~KM ABPIF ABPTK FBQ AAPBV IQODW AAHBH AAXKI AAYXX AFJKZ AKRWK CITATION |
ID | FETCH-LOGICAL-c357t-8883eedbee35d44c4750abe695021d6d3839cccbd912c470bc6a8da1bc8238b83 |
IEDL.DBID | AIKHN |
ISSN | 0167-1987 |
IngestDate | Thu Sep 26 15:17:42 EDT 2024 Sun Oct 22 16:07:50 EDT 2023 Wed Dec 27 19:26:42 EST 2023 Fri Feb 23 02:26:50 EST 2024 |
IsPeerReviewed | true |
IsScholarly | true |
Issue | 1 |
Keywords | Regression Artificial neural network Soil properties Hydraulic parameters Prediction Pedotransfer function Soil moisture Property of soil Hydraulic conductivity Regression analysis Neural network Saturated medium Soil science Water holding capacity Artificial intelligence Hydraulic properties |
Language | English |
License | CC BY 4.0 |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-c357t-8883eedbee35d44c4750abe695021d6d3839cccbd912c470bc6a8da1bc8238b83 |
Notes | http://dx.doi.org/10.1016/j.still.2005.08.011 |
PageCount | 9 |
ParticipantIDs | crossref_primary_10_1016_j_still_2005_08_011 pascalfrancis_primary_18110918 fao_agris_US201300739091 elsevier_sciencedirect_doi_10_1016_j_still_2005_08_011 |
PublicationCentury | 2000 |
PublicationDate | 2006-11-01 |
PublicationDateYYYYMMDD | 2006-11-01 |
PublicationDate_xml | – month: 11 year: 2006 text: 2006-11-01 day: 01 |
PublicationDecade | 2000 |
PublicationPlace | Amsterdam |
PublicationPlace_xml | – name: Amsterdam |
PublicationTitle | Soil & tillage research |
PublicationYear | 2006 |
Publisher | Elsevier B.V Elsevier |
Publisher_xml | – name: Elsevier B.V – name: Elsevier |
References | Ahuja, Cassel, Bruce, Barnes (bib1) 1989; 148 Apan, M., 1971. Study on the solutions of soil and water resources problems in terms of irrigation in the Erzincan Plain. Atatürk University, Faculty of Agriculture, Ph.D. Thesis, Erzurum, Turkey. Hillel (bib10) 1998 Black, C.A., 1969. Methods of soil analysis (Agronomy, a series of monographs), American Society of Agronomy, pp. 1572. Mehrotra, Mohan, Ranka (bib14) 1997 Pachepsky, Rawls (bib20) 1999; 63 Schetinin (bib26) 2003; 17 Koekkoek, Booltink (bib11) 1999; 50 Mayr, Jarvis (bib13) 1999; 91 Lin, McInnes, Wilding, Hallmark (bib12) 1999; 63 SAS Institute Inc., 1999. SAS/STAT user's guide. Ver. 8.0. SAS Institute Inc., Cary, NC. Wösten, Pachepsky, Rawls (bib33) 2001; 251 Minasny, Hopmans, Harter, Eching, Tuli, Denton (bib17) 2004; 68 Bell, Keulen (bib4) 1995; 59 Brooks, R.H., Corey, A.T., 1964. Hydraulic properties of porous media. Hydrology papers, Colorado State University, Fort Collins, Colorado. No.3. Tamari, Wösten, Ruiz-Suarez (bib28) 1996; 60 Shuh, Cline, Sweeney (bib27) 1988; 52 Haykin (bib9) 1994 Field, Parker, Powell (bib8) 1984; 138 Pachepsky, Rawls (bib21) 2003; 54 Tomasella, Hodnett, Rossato (bib29) 2000; 64 van Genuchten, M.Th., Leij, F.J., Yates, S.R., 1991. The RETC code for quantifying the hydraulic functions of unsaturated soils. EPA/600/2-91/065, U.S. Environmental Protection Agency, Ada, OK. van Genuchten (bib30) 1980; 44 Minasny, McBratney (bib16) 2002; 66 Schaap, Leij (bib24) 1998; 47 Batjes (bib3) 1996; 71 Wösten, Finke, Jansen (bib32) 1995; 66 Bouma (bib6) 1989; 9 Schaap, Leij, van Genuchten (bib25) 1998; 62 Pachepsky, Timlin, Varallyay (bib19) 1996; 60 Nemes, Schaap, Wösten (bib18) 2002 Salchow, Lal, Fausey, Ward (bib22) 1996; 73 Minasny, McBratney, Bristow (bib15) 1999; 93 Minasny (10.1016/j.still.2005.08.011_bib17) 2004; 68 Haykin (10.1016/j.still.2005.08.011_bib9) 1994 Wösten (10.1016/j.still.2005.08.011_bib33) 2001; 251 Koekkoek (10.1016/j.still.2005.08.011_bib11) 1999; 50 Field (10.1016/j.still.2005.08.011_bib8) 1984; 138 Minasny (10.1016/j.still.2005.08.011_bib16) 2002; 66 Lin (10.1016/j.still.2005.08.011_bib12) 1999; 63 Pachepsky (10.1016/j.still.2005.08.011_bib21) 2003; 54 Tomasella (10.1016/j.still.2005.08.011_bib29) 2000; 64 Mayr (10.1016/j.still.2005.08.011_bib13) 1999; 91 Pachepsky (10.1016/j.still.2005.08.011_bib20) 1999; 63 Wösten (10.1016/j.still.2005.08.011_bib32) 1995; 66 Minasny (10.1016/j.still.2005.08.011_bib15) 1999; 93 10.1016/j.still.2005.08.011_bib23 Bell (10.1016/j.still.2005.08.011_bib4) 1995; 59 Pachepsky (10.1016/j.still.2005.08.011_bib19) 1996; 60 Hillel (10.1016/j.still.2005.08.011_bib10) 1998 Salchow (10.1016/j.still.2005.08.011_bib22) 1996; 73 Schaap (10.1016/j.still.2005.08.011_bib25) 1998; 62 Bouma (10.1016/j.still.2005.08.011_bib6) 1989; 9 Schaap (10.1016/j.still.2005.08.011_bib24) 1998; 47 Mehrotra (10.1016/j.still.2005.08.011_bib14) 1997 Ahuja (10.1016/j.still.2005.08.011_bib1) 1989; 148 10.1016/j.still.2005.08.011_bib2 Shuh (10.1016/j.still.2005.08.011_bib27) 1988; 52 Schetinin (10.1016/j.still.2005.08.011_bib26) 2003; 17 van Genuchten (10.1016/j.still.2005.08.011_bib30) 1980; 44 Batjes (10.1016/j.still.2005.08.011_bib3) 1996; 71 10.1016/j.still.2005.08.011_bib7 Nemes (10.1016/j.still.2005.08.011_bib18) 2002 Tamari (10.1016/j.still.2005.08.011_bib28) 1996; 60 10.1016/j.still.2005.08.011_bib31 10.1016/j.still.2005.08.011_bib5 |
References_xml | – volume: 138 start-page: 385 year: 1984 end-page: 396 ident: bib8 article-title: Comparison of field- and laboratory-measured and predicted hydraulic properties of a soil with macropores publication-title: Soil Sci. contributor: fullname: Powell – volume: 62 start-page: 847 year: 1998 end-page: 855 ident: bib25 article-title: Neural network analysis for hierarchical prediction of soil hydraulic properties publication-title: Soil Sci. Soc. Am. J. contributor: fullname: van Genuchten – volume: 71 start-page: 31 year: 1996 end-page: 51 ident: bib3 article-title: Development of a world data set of soil water retention properties using pedotransfer rules publication-title: Geoderma contributor: fullname: Batjes – volume: 44 start-page: 892 year: 1980 end-page: 898 ident: bib30 article-title: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils publication-title: Soil Sci. Soc. Am. J. contributor: fullname: van Genuchten – volume: 66 start-page: 227 year: 1995 end-page: 237 ident: bib32 article-title: Comparison of class and continuous pedotransfer functions to generate soil hydraulic characteristics publication-title: Geoderma contributor: fullname: Jansen – year: 1994 ident: bib9 article-title: Neural Networks, A Comprehensive Foundation contributor: fullname: Haykin – volume: 63 start-page: 955 year: 1999 end-page: 961 ident: bib12 article-title: Effects of soil morphology on hydraulic properties. II. Hydraulic pedotransfer functions publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Hallmark – volume: 50 start-page: 489 year: 1999 end-page: 495 ident: bib11 article-title: Neural network models to predict soil water retention publication-title: Eur. J. Soil Sci. contributor: fullname: Booltink – volume: 60 start-page: 1732 year: 1996 end-page: 1741 ident: bib28 article-title: Testing an artificial neural network for predicting soil hydraulic conductivity publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Ruiz-Suarez – volume: 9 start-page: 177 year: 1989 end-page: 213 ident: bib6 article-title: Using soil survey data for quantitative land evaluation publication-title: Adv. Soil Sci. contributor: fullname: Bouma – volume: 93 start-page: 225 year: 1999 end-page: 253 ident: bib15 article-title: Comparison of different approaches to the development of pedotransfer functions for water-retention curves publication-title: Geoderma contributor: fullname: Bristow – volume: 17 start-page: 21 year: 2003 end-page: 31 ident: bib26 article-title: A learning algorithm for evolving Cascade Neural Networks publication-title: Neural Process. Lett. contributor: fullname: Schetinin – volume: 73 start-page: 165 year: 1996 end-page: 181 ident: bib22 article-title: Pedotransfer functions for variable alluvial soils in southern Ohio publication-title: Geoderma contributor: fullname: Ward – volume: 52 start-page: 1218 year: 1988 end-page: 1227 ident: bib27 article-title: Comparison of a laboratory procedure and a textural model for predicting in situ water retention publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Sweeney – volume: 54 start-page: 443 year: 2003 end-page: 451 ident: bib21 article-title: Soil structure and pedotransfer functions publication-title: Eur. J. Soil Sci. contributor: fullname: Rawls – volume: 66 start-page: 352 year: 2002 end-page: 361 ident: bib16 article-title: The Neuro-m method for fitting neural network parametric pedotransfer functions publication-title: Soil Sci. Soc. Am. J. contributor: fullname: McBratney – year: 2002 ident: bib18 article-title: Validation of international scale soil hydraulic pedotransfer functions for national scale applications publication-title: Symposium no. 04 on 17th WCSS, paper no. 934, poster presentation contributor: fullname: Wösten – volume: 64 start-page: 327 year: 2000 end-page: 338 ident: bib29 article-title: Pedotransfer functions for the estimation of soil water retention in Brazilian soils publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Rossato – year: 1998 ident: bib10 article-title: Environmental Soil Physics contributor: fullname: Hillel – volume: 91 start-page: 1 year: 1999 end-page: 9 ident: bib13 article-title: Pedotransfer functions to estimate soil water retention parameters for a modified Brooks-Corey type model publication-title: Geoderma contributor: fullname: Jarvis – volume: 148 start-page: 404 year: 1989 end-page: 411 ident: bib1 article-title: Evaluation of spatial distribution of hydraulic conductivity using effective porosity data publication-title: Soil Sci. contributor: fullname: Barnes – volume: 59 start-page: 865 year: 1995 end-page: 871 ident: bib4 article-title: Soil pedotransfer functions for four Mexican soils publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Keulen – volume: 47 start-page: 37 year: 1998 end-page: 42 ident: bib24 article-title: Using neural networks to predict soil water retention and soil hydraulic conductivity publication-title: Soil Till. Res. contributor: fullname: Leij – volume: 251 start-page: 123 year: 2001 end-page: 150 ident: bib33 article-title: Padotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics publication-title: J. Hydrol. contributor: fullname: Rawls – volume: 68 start-page: 417 year: 2004 end-page: 429 ident: bib17 article-title: Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Denton – year: 1997 ident: bib14 article-title: Elements of Artificial Neural Networks contributor: fullname: Ranka – volume: 63 start-page: 1748 year: 1999 end-page: 1757 ident: bib20 article-title: Accuracy and reliability of pedotransfer functions as affected by grouping soils publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Rawls – volume: 60 start-page: 727 year: 1996 end-page: 733 ident: bib19 article-title: Artificial neural networks to estimate soil water retention from easily measurable data publication-title: Soil Sci. Soc. Am. J. contributor: fullname: Varallyay – ident: 10.1016/j.still.2005.08.011_bib23 – volume: 63 start-page: 955 year: 1999 ident: 10.1016/j.still.2005.08.011_bib12 article-title: Effects of soil morphology on hydraulic properties. II. Hydraulic pedotransfer functions publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1999.634955x contributor: fullname: Lin – volume: 59 start-page: 865 year: 1995 ident: 10.1016/j.still.2005.08.011_bib4 article-title: Soil pedotransfer functions for four Mexican soils publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1995.03615995005900030034x contributor: fullname: Bell – volume: 62 start-page: 847 year: 1998 ident: 10.1016/j.still.2005.08.011_bib25 article-title: Neural network analysis for hierarchical prediction of soil hydraulic properties publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1998.03615995006200040001x contributor: fullname: Schaap – volume: 66 start-page: 352 year: 2002 ident: 10.1016/j.still.2005.08.011_bib16 article-title: The Neuro-m method for fitting neural network parametric pedotransfer functions publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj2002.0352 contributor: fullname: Minasny – volume: 47 start-page: 37 year: 1998 ident: 10.1016/j.still.2005.08.011_bib24 article-title: Using neural networks to predict soil water retention and soil hydraulic conductivity publication-title: Soil Till. Res. doi: 10.1016/S0167-1987(98)00070-1 contributor: fullname: Schaap – volume: 148 start-page: 404 year: 1989 ident: 10.1016/j.still.2005.08.011_bib1 article-title: Evaluation of spatial distribution of hydraulic conductivity using effective porosity data publication-title: Soil Sci. doi: 10.1097/00010694-198912000-00002 contributor: fullname: Ahuja – volume: 50 start-page: 489 year: 1999 ident: 10.1016/j.still.2005.08.011_bib11 article-title: Neural network models to predict soil water retention publication-title: Eur. J. Soil Sci. doi: 10.1046/j.1365-2389.1999.00247.x contributor: fullname: Koekkoek – volume: 93 start-page: 225 year: 1999 ident: 10.1016/j.still.2005.08.011_bib15 article-title: Comparison of different approaches to the development of pedotransfer functions for water-retention curves publication-title: Geoderma doi: 10.1016/S0016-7061(99)00061-0 contributor: fullname: Minasny – year: 2002 ident: 10.1016/j.still.2005.08.011_bib18 article-title: Validation of international scale soil hydraulic pedotransfer functions for national scale applications contributor: fullname: Nemes – year: 1994 ident: 10.1016/j.still.2005.08.011_bib9 contributor: fullname: Haykin – year: 1997 ident: 10.1016/j.still.2005.08.011_bib14 contributor: fullname: Mehrotra – volume: 64 start-page: 327 year: 2000 ident: 10.1016/j.still.2005.08.011_bib29 article-title: Pedotransfer functions for the estimation of soil water retention in Brazilian soils publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj2000.641327x contributor: fullname: Tomasella – ident: 10.1016/j.still.2005.08.011_bib7 – volume: 52 start-page: 1218 year: 1988 ident: 10.1016/j.still.2005.08.011_bib27 article-title: Comparison of a laboratory procedure and a textural model for predicting in situ water retention publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1988.03615995005200050003x contributor: fullname: Shuh – ident: 10.1016/j.still.2005.08.011_bib5 – volume: 251 start-page: 123 year: 2001 ident: 10.1016/j.still.2005.08.011_bib33 article-title: Padotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics publication-title: J. Hydrol. doi: 10.1016/S0022-1694(01)00464-4 contributor: fullname: Wösten – volume: 60 start-page: 727 year: 1996 ident: 10.1016/j.still.2005.08.011_bib19 article-title: Artificial neural networks to estimate soil water retention from easily measurable data publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1996.03615995006000030007x contributor: fullname: Pachepsky – volume: 63 start-page: 1748 year: 1999 ident: 10.1016/j.still.2005.08.011_bib20 article-title: Accuracy and reliability of pedotransfer functions as affected by grouping soils publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1999.6361748x contributor: fullname: Pachepsky – volume: 54 start-page: 443 year: 2003 ident: 10.1016/j.still.2005.08.011_bib21 article-title: Soil structure and pedotransfer functions publication-title: Eur. J. Soil Sci. doi: 10.1046/j.1365-2389.2003.00485.x contributor: fullname: Pachepsky – ident: 10.1016/j.still.2005.08.011_bib31 – year: 1998 ident: 10.1016/j.still.2005.08.011_bib10 contributor: fullname: Hillel – volume: 68 start-page: 417 year: 2004 ident: 10.1016/j.still.2005.08.011_bib17 article-title: Neural networks prediction of soil hydraulic functions for alluvial soils using multistep outflow data publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj2004.0417 contributor: fullname: Minasny – volume: 138 start-page: 385 year: 1984 ident: 10.1016/j.still.2005.08.011_bib8 article-title: Comparison of field- and laboratory-measured and predicted hydraulic properties of a soil with macropores publication-title: Soil Sci. doi: 10.1097/00010694-198412000-00001 contributor: fullname: Field – volume: 71 start-page: 31 year: 1996 ident: 10.1016/j.still.2005.08.011_bib3 article-title: Development of a world data set of soil water retention properties using pedotransfer rules publication-title: Geoderma doi: 10.1016/0016-7061(95)00089-5 contributor: fullname: Batjes – volume: 66 start-page: 227 year: 1995 ident: 10.1016/j.still.2005.08.011_bib32 article-title: Comparison of class and continuous pedotransfer functions to generate soil hydraulic characteristics publication-title: Geoderma doi: 10.1016/0016-7061(94)00079-P contributor: fullname: Wösten – volume: 44 start-page: 892 year: 1980 ident: 10.1016/j.still.2005.08.011_bib30 article-title: A closed-form equation for predicting the hydraulic conductivity of unsaturated soils publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1980.03615995004400050002x contributor: fullname: van Genuchten – volume: 9 start-page: 177 year: 1989 ident: 10.1016/j.still.2005.08.011_bib6 article-title: Using soil survey data for quantitative land evaluation publication-title: Adv. Soil Sci. doi: 10.1007/978-1-4612-3532-3_4 contributor: fullname: Bouma – ident: 10.1016/j.still.2005.08.011_bib2 – volume: 73 start-page: 165 year: 1996 ident: 10.1016/j.still.2005.08.011_bib22 article-title: Pedotransfer functions for variable alluvial soils in southern Ohio publication-title: Geoderma doi: 10.1016/0016-7061(96)00044-4 contributor: fullname: Salchow – volume: 17 start-page: 21 year: 2003 ident: 10.1016/j.still.2005.08.011_bib26 article-title: A learning algorithm for evolving Cascade Neural Networks publication-title: Neural Process. Lett. doi: 10.1023/A:1022935810223 contributor: fullname: Schetinin – volume: 60 start-page: 1732 year: 1996 ident: 10.1016/j.still.2005.08.011_bib28 article-title: Testing an artificial neural network for predicting soil hydraulic conductivity publication-title: Soil Sci. Soc. Am. J. doi: 10.2136/sssaj1996.03615995006000060018x contributor: fullname: Tamari – volume: 91 start-page: 1 year: 1999 ident: 10.1016/j.still.2005.08.011_bib13 article-title: Pedotransfer functions to estimate soil water retention parameters for a modified Brooks-Corey type model publication-title: Geoderma doi: 10.1016/S0016-7061(98)00129-3 contributor: fullname: Mayr |
SSID | ssj0004328 |
Score | 2.2630486 |
Snippet | Modeling water flow and solute transport in vadose zone requires knowledge of soil hydraulic properties, which are water retention and hydraulic conductivity... |
SourceID | crossref pascalfrancis fao elsevier |
SourceType | Aggregation Database Index Database Publisher |
StartPage | 108 |
SubjectTerms | Agronomy. Soil science and plant productions Artificial neural network Biological and medical sciences equations Fundamental and applied biological sciences. Psychology Hydraulic parameters hydrologic models mathematical models neural networks Pedotransfer function pedotransfer functions Prediction Regression regression analysis saturated hydraulic conductivity soil soil hydraulic properties soil physical properties Soil properties Soil science soil water retention |
Title | Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity |
URI | https://dx.doi.org/10.1016/j.still.2005.08.011 |
Volume | 90 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07T8MwED7RssCAeIryqDwwEhonzmusKlABwQKV2CLHdqCoSqu0FWLhf_BvuXMSHgMMTFVS2U19F9939ufvAE6yKDKGqDheohJHBInvJHEoHVeIHOGtVMIKz9_chsORuHoIHlZg0JyFIVplPfdXc7qdres7vXo0e7PxuHdHBHpKmd3AyuqFLVjFcCREG1b7l9fD26_jkb4tsWolvqlBIz5kaV74Ik0m9dpKfOZy_luAauVySsxJOcfBy6uqF99C0cUmbNQYkvWrx9yCFVNsw3r_sax1NMwOvA8-6wuyac7oH1VSEYwELO2HpX8zWWhWmseKDVuwmcEs1WJZUzKKedYtGSJbNitpT4euqcf5dDxhLwhUS2y-qDiTtrM5KYXifc2eXnUpl5OxYphzk6ysrVOxC6OL8_vB0KmrMDjKD6KFgymyj4E0M8YPtBBKIMaQmQmTAOGBDjWmuIlSKtMJ9_BLN1OhjLXkmYoRDmSxvwftYlqYfWCuJ2mlSYsYfSBH3_Gj3Is87ELLkPOkA6fN0KezSmwjbVhoz6m1FJXNDFKqnMl5B8LGPOkPn0kxHPzdcB-NmUq0yzwd3Xm0e0s7loidOtD9YeGv54hJm5XHB__90UNYs8s39hzjEbQX5dIcI6BZZF1onb3xbu22H9qS-EI |
link.rule.ids | 315,786,790,4521,24144,27955,27956,45618,45712 |
linkProvider | Elsevier |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07b9swED7EzpB2KJomhZ3mwaFjFIsS9RoNI4bbJlkSA9kIiqQSF4ZsyDaCLPkf_be9o6Q8hmboZFiyKIp35n1HfvoO4HueJNYSFSfIdOaJKAu9LI2V5wtRILxVWjjh-cureDIVP2-j2y0Yte_CEK2ymfvrOd3N1s2RQTOag-VsNrgmAj2lzH7kZPXiDmwTGiBe19nTC89DhK7AqhP4pp-30kOO5IV_o_m8WVlJz3zO_xWeOoVaEG9SrXDoirrmxatANP4MnxoEyYZ1J3dhy5Zf4OPwrmpUNOwe_Bk9Vxdki4LR89RCEYzkK92HI38zVRpW2buaC1uypcUc1SFZWzGKeM4pGeJatqxoR4e-U4urxWzOHhCmVnj5umZMusZWpBOKxw27fzSV2sxnmmHGTaKyrkrFPkzH5zejidfUYPB0GCVrDxPkEMNobm0YGSG0QIShchtnEYIDExtMcDOtdW4yHuBJP9exSo3iuU4RDORp-BW65aK0PWB-oGidyYgUPaBAzwmTIkgCbMKomPOsD6ft0MtlLbUhWw7ab-ksRUUzI0l1MznvQ9yaR77xGInB4P0Le2hMqdAuKzm9DmjvlvYrETn14fiNhV_6kZIyK08P_vemJ7Azubm8kBc_rn59gw9uIce90XgI3XW1sUcIbdb5sXPdvxde-Rc |
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=Comparison+of+artificial+neural+network+and+regression+pedotransfer+functions+for+prediction+of+soil+water+retention+and+saturated+hydraulic+conductivity&rft.jtitle=Soil+%26+tillage+research&rft.au=Merdun%2C+H&rft.au=%C3%87%C4%B1nar%2C+%C3%96&rft.au=Meral%2C+R&rft.au=Apan%2C+M&rft.date=2006-11-01&rft.issn=0167-1987&rft.eissn=1879-3444&rft.volume=90&rft.spage=108&rft.epage=116&rft_id=info:doi/10.1016%2Fj.still.2005.08.011&rft.externalDocID=US201300739091 |
thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0167-1987&client=summon |
thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0167-1987&client=summon |
thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0167-1987&client=summon |