Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches

Basit Öğe Kaydı
dc.authorid0000-0002-7553-9313en_US
dc.contributor.authorYilmaz, Banu
dc.contributor.authorAras, Egemen
dc.contributor.authorKankal, Murat
dc.contributor.authorNacar, Sinan
dc.date.accessioned2021-03-20T20:12:24Z
dc.date.available2021-03-20T20:12:24Z
dc.date.issued2019
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractThe main aim of the research is to use the artificial neural network (ANN) model with the artificial bee colony (ABC) and teaching-learning-based optimization (TLBO) algorithms for estimating suspended sediment loading. The stream flow per month and SSL data obtained from two stations, Inanli and Altinsu, in Coruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were used as input parameters, only SSL data were used as output parameters for all models. The successes of the ANN-ABC and ANN-TLBO models that were developed in the research were contrasted with performance of conventional ANN model trained by BP (back-propagation). In addition to these algorithms, linear regression method was applied and compared with others. Root-mean-square and mean absolute error were used as success assessing criteria for model accuracy. When the overall situation is evaluated according to errors of the testing datasets, it was found that ANN-ABC and ANN-TLBO algorithms are more outstanding than conventional ANN model trained by BP.en_US
dc.identifier.doi10.1007/s11600-019-00374-3en_US
dc.identifier.endpage1705en_US
dc.identifier.issn1895-6572
dc.identifier.issn1895-7455
dc.identifier.issue6en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1693en_US
dc.identifier.urihttp://doi.org/10.1007/s11600-019-00374-3
dc.identifier.urihttps://hdl.handle.net/20.500.12885/536
dc.identifier.volume67en_US
dc.identifier.wosWOS:000509331200017en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorAras, Egemen
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofActa Geophysicaen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial bee colonyen_US
dc.subjectCoruh river basinen_US
dc.subjectEstimationen_US
dc.subjectSuspended sediment loadingen_US
dc.subjectTeaching-learning-based optimizationen_US
dc.titlePrediction of suspended sediment loading by means of hybrid artificial intelligence approachesen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
İnşaat Mühendisliği Bölümü Yayın Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu