Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils

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dc.authorid0000-0002-4767-6660en_US
dc.contributor.authorVaheddoost, Babak
dc.contributor.authorGuan, Yiqing
dc.contributor.authorMohammadi, Babak
dc.date.accessioned2021-03-20T20:09:35Z
dc.date.available2021-03-20T20:09:35Z
dc.date.issued2020
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractField capacity (FC) and permanent wilting point (PWP) are two important properties of the soil when the soil moisture is concerned. Since the determination of these parameters is expensive and time-consuming, this study aims to develop and evaluate a new hybrid of artificial neural network model coupled with a whale optimization algorithm (ANN-WOA) as a meta-heuristic optimization tool in defining the FC and the PWP at the basin scale. The simulated results were also compared with other core optimization models of ANN and multilinear regression (MLR). For this aim, a set of 217 soil samples were taken from different regions located across the West and East Azerbaijan provinces in Iran, partially covering four important basins of Lake Urmia, Caspian Sea, Persian Gulf-Oman Sea, and Central-Basin of Iran. Taken samples included portion of clay, sand, and silt together with organic matter, which were used as independent variables to define the FC and the PWP. A 80-20 portion of the randomly selected independent and dependent variable sets were used in calibration and validation of the predefined models. The most accurate predictions for the FC and PWP at the selected stations were obtained by the hybrid ANN-WOA models, and evaluation criteria at the validation phases were obtained as 2.87%, 0.92, and 2.11% respectively for RMSE, R-2, and RRMSE for the FC, and 1.78%, 0.92, and 10.02% respectively for RMSE, R-2, and RRMSE for the PWP. It is concluded that the organic matter is the most important variable in prediction of FC and PWP, while the proposed ANN-WOA model is an efficient approach in defining the FC and the PWP at the basin scale.en_US
dc.identifier.doi10.1007/s11356-020-07868-4en_US
dc.identifier.endpage13141en_US
dc.identifier.issn0944-1344
dc.identifier.issn1614-7499
dc.identifier.issue12en_US
dc.identifier.pmid32016876en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage13131en_US
dc.identifier.urihttp://doi.org/10.1007/s11356-020-07868-4
dc.identifier.urihttps://hdl.handle.net/20.500.12885/475
dc.identifier.volume27en_US
dc.identifier.wosWOS:000515861200006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorVaheddoost, Babak
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEnvironmental Science And Pollution Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectHybrid modelen_US
dc.subjectHydropedologyen_US
dc.subjectMeta-heuristic algorithmen_US
dc.subjectSoil moistureen_US
dc.titleApplication of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soilsen_US
dc.typeArticleen_US

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