Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river

Abstract

Predictions of sediment load are required in a wide spectrum of problems such as design of the dead volume of a dam, sediment transport in the river, design of stable channels, and dredging needs. Researchers have used the regression between sediment concentration and water discharge. Such relationships are obtained through the application of regression analysis in many studies. Unfortunately, in the classical regression approach to determine sediment concentration---water discharge relationships, internal uncertainties are not taken explicitly into consideration. Therefore, researchers look for non-linear methods to estimate sediment load such as artificial neural network (ANN) methods to solve non-linear problems. The main purpose of this paper is to optimize the ANN connection weights with novel social-based algorithm (SBA) to realize the sediment in Maroon river. The SBA tries to capture several people in different types of countries. They try to reach high levels. The approach illustrated feed-forward neural network optimization for sediment estimation of four Maroon river stations in Iran, which was called FF-SBA. Three inputs were presented in each station: length of river, discharge and debit. Sediment parameter on the same station is measured as the output parameter. Results of optimization algorithms such as the genetic algorithm, particle swarm optimization and imperialist competitive algorithm were compared with the SBA results, and it was found that the FF-SBA model exhibited more capability, flexibility, and accuracy in sediment training, testing, and forecasting steps for the Maroon river in Iran.