SUSPENDED SEDIMENT LOAD PREDICTION IN RIVERS BY USING HEURISTIC REGRESSION AND HYBRID ARTIFICIAL INTELLIGENCE MODELS

Authors : Banu YILMAZ, Egemen ARAS, Murat KANKAL, Sinan NACAR

Pages : 703-714

View : 53 | Download : 10

Publication Date : 2021-06-01

Article Type : Research Paper

Abstract :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 insert ignore into journalissuearticles values(SSL); obtained from Karşıköy Gauging Station, located on Çoruh River in the north-eastern of Turkey, modelled with different regression methods insert ignore into journalissuearticles values(multiple regression, multivariate adaptive regression splines); and artificial neural network insert ignore into journalissuearticles values(ANN); insert ignore into journalissuearticles values(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.
Keywords : Artificial intelligence, Çoruh river basin, regression analysis, river hydraulics, suspended sediment load

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