Application Research of Support Vector Machine Based on Particle Swarm Optimization in Runoff Forecasting

Abstract:

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%.

• Periodical: Applied Mechanics and Materials (Volumes 226-228)
• Pages: 2303-2307
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.226-228.2303
• Online since: November 2012
• Authors: Li Xue Wang, Li Na Wang, Guo Feng Li, Ce Luan, Fei Fei Sun
• Keywords: Dahuofang Reservoir, Parameter Optimization, Particle Swarm Optimization (PSO), Runoff Forecast, Support Vector Machine (SVM)

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DOI: 10.1017/cbo9780511801389