Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods

In this paper, effective reservoir inflow forecasting models based on the support vector machine (SVM), which is a novel kind of neural networks (NNs), are proposed. Based on statistical learning theory, the SVMs have three advantages over back-propagation networks (BPNs), which are the most frequently used convectional NNs. Firstly, SVMs have better generalization ability. Secondly, the architectures and the weights of the SVMs are guaranteed to be unique and globally optimal. Finally, SVMs are trained much more rapidly. An application is conducted to clearly demonstrate these three advantages. The results indicate that the proposed SVM-based models are more well-performed, robust and efficient than the existing BPN-based models. In addition to using SVMs instead of BPNs, typhoon characteristics, which are seldom regarded as key input for inflow forecasting, are added to the proposed models to further improve the long lead-time forecasting during typhoon-warning periods. A comparison between models with and without typhoon characteristics is also presented to confirm that the addition of typhoon characteristics significantly improves the forecasting performance for long lead-time forecasting. In conclusion, the typhoon characteristics should be used as input to the reservoir inflow forecasting. The proposed SVM-based models are recommended as an alternative to the existing models because of their accuracy, robustness and efficiency. The proposed modeling technique is expected to be useful to improve the reservoir inflow forecasting.