Application of optimized Artificial and Radial Basis neural networks by using modified Genetic Algorithm on discharge coefficient prediction of modified labyrinth side weir with two and four cycles. (February 2020) - Zaji, Amir Hossein; Bonakdari, Hossein; Khameneh, Hamed Zahedi; Khodashenas, Saeed Reza
Graphical abstract: Genetic Algorithm Artificial neural network (GAA) and Genetic Algorithm Radial Basis neural network (GARB) were introduced and compared for prediction of the discharge coefficient. Highlights: A new method is developed for discharge coefficient prediction in labyrinth side weir. Genetic Algorithm Artificial neural network (GAA) and Genetic Algorithm Radial Basis neural network (GARB) were introduced. Best input combinations for each of the GAA and GARB model were surveyed. GARB method could successfully predict the accurate discharge coefficient. Abstract: Determining the discharge coefficient is one of the most important processes in designing side weirs. In this study, the structure of Artificial Neural Network (ANN) and Radial Basis Neural Network (RBNN) methods are optimized by a modified Genetic Algorithm (GA). So two new hybrid methods of Genetic Algorithm Artificial neural network (GAA) and Genetic Algorithm Radial Basis neural network (GARB), were introduced and compared with each other. The modified GA was used to find the neuron number in the hidden layers of the ANN and to find the spread value and the neuron number of the RBNN method, as well. GAA and GARB were tested for predicting the discharge coefficient of a modified labyrinth side weir he GARB method could successfully predict the accurate discharge coefficient even in cases where there is a limited number of train datasets available.