{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T18:17:57Z","timestamp":1776709077649,"version":"3.51.2"},"reference-count":71,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"vor","delay-in-days":192,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>\n                    Evapotranspiration represents the water requirement of plants during their growing season, and its accurate measurement at the farm is essential for agricultural water planners and managers. Field measurements of evapotranspiration have always been associated with many difficulties that have led researchers to seek a way to remotely measure this component in horticultural and agricultural areas. This study aims to investigate an indirect approach for daily rice crop evapotranspiration (ETc) measurement by machine learning (ML) techniques and the least available climatic variables. For this purpose, daily meteorological variables were obtained from three ground meteorological stations in rice cultivation regions of northern Iran during 2003\u20132016. The ETc rates were calculated by seven meteorological variables, the FAO\u201056 Penman\u2010Monteith equation, and the regional calibrated rice crop coefficient and considered as the reference data. The MLs, including Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Regression Neural Network (GRNN), and Group Method of Data Handling (GMDH), were utilized for ETc modeling. Different input combinations were applied, based on the use of minimum effective variables as input. Results showed that the models showed the most accurate performances in the input combination of four climatic variables: sunshine duration, maximum temperature, relative humidity, and wind speed. Investigating the accuracy of models in rice growth phases showed that the least estimation error belonged to the initial growing stage, which increased during the mid\u2010season and late\u2010season growing stages. A comparison of the models showed that the GMDH model performed better against the other competitors. For this model, both the Nash\u2010Sutcliffe (NS) coefficient and\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    were greater than 0.98, and the Root Mean Square Error (RMSE) ranged between 0.214 and 0.234\u2009mm per day in all stations. The current approach showed promising results in rice evapotranspiration modeling by only four common meteorological variables and can be reliably applied for indirect measurement of this variable over the rice farms. The studied approach will have research value for rice and other crops in similar\/different climatic conditions.\n                  <\/jats:p>","DOI":"10.1155\/2022\/4534822","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T20:20:08Z","timestamp":1657657208000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimating Daily Rice Crop Evapotranspiration in Limited Climatic Data and Utilizing the Soft Computing Algorithms MLP, RBF, GRNN, and GMDH"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5640-865X","authenticated-orcid":false,"given":"Pouya","family":"Aghelpour","sequence":"first","affiliation":[]},{"given":"Hadigheh","family":"Bahrami-Pichaghchi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0822-0046","authenticated-orcid":false,"given":"Farzaneh","family":"Karimpour","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2022,7,12]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120348"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.2166\/ws.2021.163"},{"key":"e_1_2_9_3_2","unstructured":"AllenR. G. PereiraL. S. RaesD. andSmithM. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56 \u201d Fao 1998 300 no. 9 Rome D05109."},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-020-03505-9"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-021-03578-0"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106491"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1002\/met.1841"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00024-020-02473-5"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2020.125509"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)he.1943-5584.0001963"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-017-1853-9"},{"key":"e_1_2_9_12_2","first-page":"73","volume-title":"Sensitivity analysis of FAO-56 penman-monteith reference evapotranspiration estimates using Monte Carlo simulations","author":"Nandagiri G. M.","year":"2018"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-017-1857-5"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9437(2002)128:4(224)"},{"key":"e_1_2_9_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-019-02966-x"},{"key":"e_1_2_9_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2016.11.011"},{"key":"e_1_2_9_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2014.03.014"},{"key":"e_1_2_9_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106547"},{"key":"e_1_2_9_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.04.085"},{"key":"e_1_2_9_20_2","doi-asserted-by":"publisher","DOI":"10.2166\/nh.2019.060"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2019.03.015"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2016.02.026"},{"key":"e_1_2_9_23_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)IR.1943-4774.0001471"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2019.123958"},{"key":"e_1_2_9_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2018.02.060"},{"key":"e_1_2_9_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106145"},{"key":"e_1_2_9_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106622"},{"key":"e_1_2_9_28_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-018-2390-z"},{"key":"e_1_2_9_29_2","doi-asserted-by":"publisher","DOI":"10.1623\/hysj.51.6.1092"},{"key":"e_1_2_9_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhydrol.2020.125087"},{"key":"e_1_2_9_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2017.01.027"},{"key":"e_1_2_9_32_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11269-013-0479-9"},{"key":"e_1_2_9_33_2","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.6837"},{"key":"e_1_2_9_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00703-012-0205-9"},{"key":"e_1_2_9_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12205-015-0556-0"},{"key":"e_1_2_9_36_2","doi-asserted-by":"publisher","DOI":"10.2166\/nh.2010.015"},{"key":"e_1_2_9_37_2","doi-asserted-by":"publisher","DOI":"10.1002\/hyp.7221"},{"key":"e_1_2_9_38_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9437(2003)129:3(214)"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1061\/(asce)0733-9437(2005)131:4(316)"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2020.106177"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11600-020-00446-9"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.2166\/wcc.2018.003"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106227"},{"key":"e_1_2_9_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2015.02.010"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1007\/s42452-020-04069-z"},{"key":"e_1_2_9_46_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.agwat.2019.105875"},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2018.05.147"},{"key":"e_1_2_9_48_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-020-01949-z"},{"key":"e_1_2_9_49_2","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)HE.1943-5584.0002059"},{"key":"e_1_2_9_50_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijgi9120701"},{"key":"e_1_2_9_51_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rse.2018.04.001"},{"key":"e_1_2_9_52_2","doi-asserted-by":"publisher","DOI":"10.1007\/s12517-017-3211-x"},{"key":"e_1_2_9_53_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-019-01761-4"},{"key":"e_1_2_9_54_2","doi-asserted-by":"publisher","DOI":"10.5194\/hessd-7-3691-2010"},{"key":"e_1_2_9_55_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12203437"},{"key":"e_1_2_9_56_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00382-022-06341-x"},{"key":"e_1_2_9_57_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-012-0741-8"},{"key":"e_1_2_9_58_2","first-page":"159","article-title":"Comparison of water requirement and crop coefficient first and second crops of rice varieties of Tarom Hashemi (Khazar Abad area)","volume":"9","author":"Babaee M.","year":"2019","journal-title":"Irrig. Water Eng."},{"key":"e_1_2_9_59_2","first-page":"95","article-title":"Determination of evapotranspiration and crop coefficient of two rice cultivars in mordab plain (guilan province)","volume":"18","author":"Modabberi H.","year":"2014","journal-title":"JWSS-Isfahan Univ. Technol."},{"key":"e_1_2_9_60_2","first-page":"95","article-title":"Derivation of crop coefficients of three rice varieties based on ETo estimation method in Rasht region","volume":"3","author":"Pirmoradian N.","year":"2013","journal-title":"Cereal Res"},{"key":"e_1_2_9_61_2","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6610228"},{"key":"e_1_2_9_62_2","article-title":"Neural networks for machine learning lecture 6a overview of mini-batch gradient descent","volume":"14","author":"Hinton G.","year":"2012","journal-title":"Cited on"},{"key":"e_1_2_9_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2018.06.033"},{"key":"e_1_2_9_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-021-02011-2"},{"key":"e_1_2_9_65_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.marstruc.2005.09.003"},{"key":"e_1_2_9_66_2","doi-asserted-by":"publisher","DOI":"10.1109\/72.97934"},{"key":"e_1_2_9_67_2","doi-asserted-by":"crossref","unstructured":"LiC. ZhangJ. andWangS. Comparative study on input-expansion-based improved general regression neural network and levenberg-marquardt BP network Lecture Notes in Computer Science Proceedings of the International Conference on Intelligent Computing 2006 Springer Berlin Germany 83\u201393 https:\/\/doi.org\/10.1007\/11816157_9.","DOI":"10.1007\/11816157_9"},{"key":"e_1_2_9_68_2","doi-asserted-by":"publisher","DOI":"10.3390\/atmos12091154"},{"key":"e_1_2_9_69_2","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(70)90092-0"},{"key":"e_1_2_9_70_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00477-022-02249-4"},{"key":"e_1_2_9_71_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00704-015-1522-y"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/4534822","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1155\/2022\/4534822","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/4534822","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T21:13:12Z","timestamp":1769116392000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/4534822"}},"subtitle":[],"editor":[{"given":"Zhichao","family":"Jiang","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":71,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/4534822"],"URL":"https:\/\/doi.org\/10.1155\/2022\/4534822","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2021-11-12","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"4534822"}}