Using Adaptive Chaotic Grey Wolf Optimization for the daily streamflow prediction

Abstract

Due to the importance of water resources management that requires the optimal model for streamflow prediction, the study of water flow prediction is of great importance. Hence, the present paper aims to introduce an innovative model that can predict streamflow with the highest accuracy. Accordingly, a multilayer perceptron neural network whose optimum neuron number is specified using the evaluation metrics is considered for simulation. The dataset used for this purpose consists of 80% experimental and 20% numerical data. Accordingly, the MLP network used in this research is optimized using various optimizers, namely Particle Swarm Optimization, Genetic Algorithm, Grey Wolf Optimization, Chaotic Grey Wolf Optimization, Advanced Grey Wolf Optimization, and Adaptive Chaotic Grey Wolf Optimization. Considering the statistical results, Adaptive Chaotic Grey Wolf Optimization is introduced as the leading optimizer. The obtained results highlight that Adaptive Chaotic Grey Wolf Optimization used for optimizing the training process of the MLP network with 15 neurons can predict the daily streamflow with the best standard error, mean square error, mean absolute percent error, mean absolute error, and normalized mean square error of about 5.3126, 2.3049, 0.6684, 1.1038, and 0.4483, respectively.