SUSPENDED SEDIMENT LOAD PREDICTION IN RIVERS BY USING HEURISTIC REGRESSION AND HYBRID ARTIFICIAL INTELLIGENCE MODELS
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Yildiz Technical Univ
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Accurate prediction of amount of sediment load in rivers is extremely important for river hydraulics. The solution of the problem has been become complicated since the explanation of hydraulic phenomenon between the flow and the sediment on the river is dependent many parameters. The usage of different regression methods and artificial intelligence techniques allows the development of predictions as the traditional methods do not give enough accurate results. In this study, data of the flow and suspended sediment load (SSL) obtained from Karsikoy Gauging Station, located on Coruh River in the north-eastern of Turkey, modelled with different regression methods (multiple regression, multivariate adaptive regression splines) and artificial neural network (ANN) (ANN-back propagation, ANN teaching-learning-based optimization algorithm and ANN-artificial bee colony). When the results were evaluated, it was seen that the models of ANN method were close to each other and gave better results than the regression models. It is concluded that these models of ANN method can be used successfully in estimating the SSL.
Açıklama
Anahtar Kelimeler
Artificial intelligence, Coruh river basin, regression analysis, river hydraulics, suspended sediment load
Kaynak
Sigma Journal Of Engineering And Natural Sciences-Sigma Muhendislik Ve Fen Bilimleri Dergisi
WoS Q Değeri
N/A
Scopus Q Değeri
Cilt
38
Sayı
2
