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dc.contributor.authorTasan, Sevda
dc.contributor.authorDemir, Yusuf
dc.date.accessioned2020-06-21T12:18:25Z
dc.date.available2020-06-21T12:18:25Z
dc.date.issued2020
dc.identifier.issn0010-3624
dc.identifier.issn1532-2416
dc.identifier.urihttps://doi.org/10.1080/00103624.2020.1729374
dc.identifier.urihttps://hdl.handle.net/20.500.12712/10198
dc.descriptionTASAN, Sevda/0000-0002-4335-4074en_US
dc.descriptionWOS: 000515046300001en_US
dc.description.abstractSoil hydraulic parameters like moisture content at field capacity and permanent wilting point constitute significant input parameters of various biophysical models and agricultural practices (irrigation timing and amount of irrigation to be applied). In this study, the performance of three different methods (Multiple linear regression - MLR, Artificial Neural Network - ANN and Adaptive Neuro-Fuzzy Inference System - ANFIS) with different input parameters in prediction of field capacity and permanent wilting point from easily obtained soil characteristics were compared. Correlation analysis indicated that clay content, sand content, cation exchange capacity, CaCO3, and organic matter had significant correlations with FC and PWP (p < .01). Validation results revealed that the ANN model with the greatest R-2 and the lowest MAE and RMSE value exhibited better performance for prediction of FC and PWP than the MLR and ANFIS models. ANN model had R-2 = 0.83, MAE = 2.36% and RMSE = 3.30% for FC and R-2 = 0.81, MAE = 2.15%, RMSE = 2.89% for PWP in training dataset; R-2 = 0.80, MAE = 2.27%, RMSE = 3.12% for FC and R-2 = 0.83, MAE = 1.84%, RMSE = 2.40% for PWP in testing dataset. Also, Bayesian Regularization (BR) algorithm exhibited better performance for both FC and PWP than the other training algorithms.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [116O715]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant [Number 116O715].en_US
dc.language.isoengen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.isversionof10.1080/00103624.2020.1729374en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectWorden_US
dc.subjectfield capacityen_US
dc.subjectpermanent wilting pointen_US
dc.subjectmultiple-linear regressionen_US
dc.subjectartificial neural networken_US
dc.subjectadaptive neuro-fuzzy inference systemen_US
dc.titleComparative Analysis of MLR, ANN, and ANFIS Models for Prediction of Field Capacity and Permanent Wilting Point for Bafra Plain Soilsen_US
dc.typearticleen_US
dc.contributor.departmentOMÜen_US
dc.identifier.volume51en_US
dc.identifier.issue5en_US
dc.identifier.startpage604en_US
dc.identifier.endpage621en_US
dc.relation.journalCommunications in Soil Science and Plant Analysisen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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