Prediction of rainfall runoff-induced sediment load from bare land surfaces by generalized regression neural network and empirical model

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dc.contributor.authorTayfur, Gokmen
dc.contributor.authorAksoy, Hafzullah
dc.contributor.authorEris, Ebru
dc.date.accessioned2020-12-01T12:04:50Z
dc.date.available2020-12-01T12:04:50Z
dc.date.issued2020
dc.departmentEge Üniversitesien_US
dc.description.abstractBased on three rainfall run-off-induced sediment transport data for bare surface experimental plots, the generalized regression neural network (GRNN) and empirical models were developed to predict sediment load. Rainfall intensity, slope, rainfall duration, soil particle median diameter, clay content of the soil, rill density and soil particle mass density constituted the input variables of the models while sediment load was the target output. the GRNN model was trained and tested. the GRNN model was found successful in predicting sediment load. Sensitivity analysis by the GRNN model revealed that slope and rainfall duration were the most sensitive parameters. in addition to the GRNN model, two empirical models were proposed: (1) in the first empirical model, all the input variables were related to the sediment load, and (2) in the second empirical model, only rainfall intensity, slope and rainfall duration were related to the sediment load. the empirical models were calibrated and validated. At the calibration stage, the coefficients and the exponents of the empirical models were obtained using the genetic algorithm optimization method. the validated empirical models were also applied to two more experimental data sets: (1) one data set was from a field experiment, and (2) one set was from a laboratory experiment. the results indicated the success of the empirical models in predicting sediment load from bare land surfaces.en_US
dc.identifier.doi10.1111/wej.12442
dc.identifier.endpage76en_US
dc.identifier.issn1747-6585
dc.identifier.issn1747-6593
dc.identifier.issue1en_US
dc.identifier.startpage66en_US
dc.identifier.urihttps://doi.org/10.1111/wej.12442
dc.identifier.urihttps://hdl.handle.net/11454/62760
dc.identifier.volume34en_US
dc.identifier.wosWOS:000513489100015en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofWater and Environment Journalen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectbare slopeen_US
dc.subjectempirical modelen_US
dc.subjectgenetic algorithmen_US
dc.subjectGRNNen_US
dc.subjectsediment loaden_US
dc.titlePrediction of rainfall runoff-induced sediment load from bare land surfaces by generalized regression neural network and empirical modelen_US
dc.typeArticleen_US

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