An Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Models

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dc.contributor.authorBuyukyildiz, Meral
dc.contributor.authorKumcu, Serife Yurdagul
dc.date.accessioned2020-03-26T19:33:46Z
dc.date.available2020-03-26T19:33:46Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSediment transport in streams and rivers takes two forms as suspended load and bed load. Suspended load comprises sand + silt + clay-sized particles that are held in suspension due to the turbulence and will only settle when the stream velocity decreases, such as when the streambed becomes flatter, or the streamflow into a pond or lake. The sources of the suspended sediments are the sediments transported from the river basin by runoff or wind and the eroded sediments of the river bed and banks. Suspended-sediment load is a key indicator for assessing the effect of land use changes, water quality studies and engineering practices in watercourses. Measuring suspended sediment in streams is real sampling and the collection process is both complex and expensive. In recent years, artificial intelligence methods have been used as a predictor for hydrological phenomenon namely to estimate the amount of suspended sediment. In this paper the abilities of Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Adaptive Network Based Fuzzy Inference System (ANFIS) models among the artificial intelligence methods have been investigated to estimate the suspended sediment load (SSL) in Ispir Bridge gauging station on Coruh River (station number: 2316). Coruh River is located in the northern east part of Turkey and it is one of the world"s the fastest, the deepest and the largest rivers of the Coruh Basin. In this study, in order to estimate the suspended sediment load, different combinations of the streamflow and the SSL were used as the model inputs. Its results accuracy was compared with the results of conventional correlation coefficient analysis between input and output variables and the best combination was identified. Finally, in order to predict SSL, the SVM, ANFIS and various ANNs models were used. The reliability of SVM, ANFIS and ANN models were determined based on performance criteria such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Efficiency Coefficient (EC) and Determination Coefficient (R-2).en_US
dc.identifier.doi10.1007/s11269-017-1581-1en_US
dc.identifier.endpage1359en_US
dc.identifier.issn0920-4741en_US
dc.identifier.issn1573-1650en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1343en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s11269-017-1581-1
dc.identifier.urihttps://hdl.handle.net/20.500.12395/34781
dc.identifier.volume31en_US
dc.identifier.wosWOS:000397923700016en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofWATER RESOURCES MANAGEMENTen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectSuspended sediment loaden_US
dc.subjectSupport vector machineen_US
dc.subjectArtificial neural networken_US
dc.subjectAdaptive network based fuzzy inference systemen_US
dc.subjectCoruh Riveren_US
dc.titleAn Estimation of the Suspended Sediment Load Using Adaptive Network Based Fuzzy Inference System, Support Vector Machine and Artificial Neural Network Modelsen_US
dc.typeArticleen_US

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