Application research of support vector machine based on particle swarm optimization in runoff forecasting

In view of the little sample, less data problems, mid-and-long term hydrologic forecasting is a case of which, Support Vector Machine (SVM) can solve this kind of problems perfectly. This paper introduced the basic optimization procedure and PSO-SVM modeling procedure. The PSO-SVM model has been applied in forecasting the monthly runoff of Dahuofang reservoir. The comparison between PSO-SVM and not-optimized SVM implied that the PSO-SVM has a fast convergence speed and strong generalization capability, also the related error has been decreased from 15.5% to 11.9%.