{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:52:34Z","timestamp":1774968754775,"version":"3.50.1"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T00:00:00Z","timestamp":1620172800000},"content-version":"vor","delay-in-days":124,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>Infiltration is a vital phenomenon in the water cycle, and consequently, estimation of infiltration rate is important for many hydrologic studies. In the present paper, different data\u2010driven models including Multiple Linear Regression (MLR), Generalized Reduced Gradient (GRG), two Artificial Intelligence (AI) techniques (Artificial Neural Network (ANN) and Multigene Genetic Programming (MGGP)), and the hybrid MGGP\u2010GRG have been applied to estimate the infiltration rates. The estimated infiltration rates were compared with those obtained by empirical infiltration models (Horton\u2019s model, Philip\u2019s model, and modified Kostiakov\u2019s model) for the published infiltration data. Among the conventional models considered, Philip\u2019s model provided the best estimates of infiltration rate. It was observed that the application of the hybrid MGGP\u2010GRG model and MGGP improved the estimates of infiltration rates as compared to conventional infiltration model, while ANN provided the best prediction of infiltration rates. To be more specific, the application of ANN and the hybrid MGGP\u2010GRG reduced the sum of square of errors by 97.86% and 81.53%, respectively. Finally, based on the comparative analysis, implementation of AI\u2010based models, as a more accurate alternative, is suggested for estimating infiltration rates in hydrological models.<\/jats:p>","DOI":"10.1155\/2021\/9945218","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T18:08:49Z","timestamp":1620238129000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["A Comparative Analysis of Data\u2010Driven Empirical and Artificial Intelligence Models for Estimating Infiltration Rates"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4295-004X","authenticated-orcid":false,"given":"Mohammad","family":"Zakwan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5022-1026","authenticated-orcid":false,"given":"Majid","family":"Niazkar","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,5,5]]},"reference":[{"key":"e_1_2_7_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pisc.2016.06.064"},{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.1111\/wej.12435"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1080\/00221686.2004.9628321"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.2478\/v10098-010-0001-5"},{"key":"e_1_2_7_5_2","doi-asserted-by":"publisher","DOI":"10.2478\/johh-2020-0006"},{"key":"e_1_2_7_6_2","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.1257"},{"key":"e_1_2_7_7_2","first-page":"120","article-title":"Optimization of Infiltration parameters in hydrology","volume":"4","author":"Deep K.","year":"2008","journal-title":"World Journal of Modelling and Simulation"},{"key":"e_1_2_7_8_2","first-page":"129","article-title":"A new method for estimating parameters of Kostiakov and Modified Kostiakov infiltration equations","volume":"15","author":"Haghiabi A.","year":"2011","journal-title":"World Applied Science Journal"},{"key":"e_1_2_7_9_2","doi-asserted-by":"publisher","DOI":"10.20897\/awet\/68347"},{"key":"e_1_2_7_10_2","doi-asserted-by":"publisher","DOI":"10.1515\/johh-2015-0012"},{"key":"e_1_2_7_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-017-1694-6"},{"key":"e_1_2_7_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2018.09.043"},{"key":"e_1_2_7_13_2","doi-asserted-by":"publisher","DOI":"10.1029\/2018wr022643"},{"key":"e_1_2_7_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/w11050910"},{"key":"e_1_2_7_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13201-020-01276-2"},{"key":"e_1_2_7_16_2","doi-asserted-by":"publisher","DOI":"10.2166\/wst.2020.369"},{"key":"e_1_2_7_17_2","doi-asserted-by":"publisher","DOI":"10.2166\/ws.2020.244"},{"key":"e_1_2_7_18_2","doi-asserted-by":"publisher","DOI":"10.2166\/hydro.2020.129"},{"key":"e_1_2_7_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05567-3"},{"key":"e_1_2_7_20_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6627011"},{"key":"e_1_2_7_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-019-02384-8"},{"key":"e_1_2_7_22_2","doi-asserted-by":"publisher","DOI":"10.1002\/ird.2332"},{"key":"e_1_2_7_23_2","doi-asserted-by":"publisher","DOI":"10.17159\/wsa\/2019.v45.i3.6737"},{"key":"e_1_2_7_24_2","doi-asserted-by":"publisher","DOI":"10.1080\/24749508.2018.1481633"},{"key":"e_1_2_7_25_2","first-page":"44","article-title":"Support vector regression-based modeling of cumulative infiltration of sandy soil","volume":"26","author":"Sihag P.","year":"2020","journal-title":"ISH Journal of Hydraulic Engineering"},{"key":"e_1_2_7_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.catena.2020.104715"},{"key":"e_1_2_7_27_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-020-06245-2"},{"key":"e_1_2_7_28_2","doi-asserted-by":"publisher","DOI":"10.1080\/02626667.2019.1659965"},{"key":"e_1_2_7_29_2","doi-asserted-by":"publisher","DOI":"10.2136\/sssaj1939.036159950003000C0066x"},{"key":"e_1_2_7_30_2","doi-asserted-by":"publisher","DOI":"10.1097\/00010694-195705000-00002"},{"key":"e_1_2_7_31_2","first-page":"15","volume-title":"Sixth Commission","author":"Kostiakov A. 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