Streamflow forecasting by SVM with quantum behaved particle swarm optimization

Accurate forecasting of streamflows has been one of the most important issues as it plays a key role in allotment of water resources. However, the information of streamflow presents a challenging situation; the streamflow forecasting involves a rather complex nonlinear data pattern. In the recent years, the support vector machine has been used widely to solve nonlinear regression and time series problems. This study investigates the accuracy of the hybrid SVM-QPSO model (support vector machine-quantum behaved particle swarm optimization) in predicting monthly streamflows. The proposed SVM-QPSO model is employed in forecasting the streamflow values of Vijayawada station and Polavaram station of Andhra Pradesh in India. The SVM model with various input structures is constructed and the best structure is determined using normalized mean square error (NMSE) and correlation coefficient (R). Further quantum behaved particle swarm optimization function is adapted in this study to determine the optimal values of SVM parameters by minimizing NMSE. Later, the performance of the SVM-QPSO model is compared thoroughly with the popular forecasting models. The results indicate that SVM-QPSO is a far better technique for predicting monthly streamflows as it provides a high degree of accuracy and reliability. (C) 2012 Elsevier B.V. All rights reserved.