Mühendislik ve Doğa Bilimleri Fakültesi

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    Estimation of the Roller Length of Submerged Hydraulic Jumps Using Artificial Intelligence Methods
    (Konya Technical University, 2021) Gümüş, Veysel; Yoluk, İbrahim Mahmut; Şimşek, Oğuz; Soydan, Göksu
    In this study, the roller length of the submerged hydraulic jump that occurred in a downstream of the laboratory canal with a sluice gate is experimentally determined. Experimentally, the roller length of the submerged hydraulic jump is estimated by using artificial intelligence methods and Multiple Linear Regression (MLR) method using the gate opening height, tail water height and froude numbers. Artificial Neural Networks (ANN), Adaptive Network Based Fuzzy Inference System (ANFIS) and Genexpression Programming (GEP) methods have been preferred as artificial intelligence methods.The coefficient of determination (R2), Mean Square Error (MSE) and Mean Absolute Relative Error (MARE) parameters are used to determine the success performance of methods in estimating the length of the roller zone. As a result of the study, it is determined that ANN and ANFIS methods, which use gate opening height, tail water height and froude number as input parameters, are quite successful in determining the roller length of the submerged hydraulic jump.