Predicting Monthly Streamflow Using Artificial Neural Networks and Wavelet Neural Networks Models

Abstract

Improving predicting methods for streamflow series is an important task for the water resource planning, management, and agriculture process. This study demonstrates the development and effectiveness of a new hybrid model for streamflow predicting. In the present study, artificial neural networks (ANNs) coupled with wavelet transform, namely Additive Wavelet Transform (AWT), are proposed. Comparative analyses of Discrete wavelet transform (DWT) based ANN and conventional ANN techniques with the proposed method were presented. The analysis of these models was performed with monthly streamflow series for four stations on the coruh Basin, which is located in northeastern Turkey. The Bayesian regularization backpropagation training algorithm was employed for the optimization of the ANN network. The predicted results of the models were analyzed by the root mean square error (RMSE), Akaike information criterion (AIC), and coefficient of determination (R-2). The obtained revealed that the proposed hybrid model represents significant accuracy compared to other models, and thus it can be a useful alternative approach for predicting studies.