Training Artificial Neural Network Using Hybrid Optimization Algorithm for Rainfall-Runoff Forecasting

Abstract

In this paper, a hybrid optimization algorithm is proposed to train the initial connection weights and thresholds of artificial neural network (ANN) by incorporating Simulated Annealing algorithm (SA) into Genetic Algorithm (GA), and then the Back Propagation (BP) algorithm is applied to adjust the final weights and biases, namely HGASA-ANN. Finally, a numerical example of daily rainfall-runoff data is used to elucidate the forecasting performance of the proposed HGASA-ANN model. The GASA is employed to accelerate the training speed and helps to avoid premature convergence and permutation problems. The HGASA-NN can make use of not only strong global searching ability of the GASA, but also strong local searching ability of the BP algorithm. The forecasting results indicate that the proposed model yields more accurate forecasting results than the back-propagation neural network and pure GA training artificial neural network. Therefore, the HGASA-ANN model is a promising alternative for rainfall-runoff forecasting.