Study on Ice Regime Forecast Based on SVR Optimized by Particle Swarm Optimization Algorithm

• 作者: RONG Fei WANG Fu-qiang
• 来源期刊: 气象与环境研究：英文版
• 学科: 地球科学
• 关键词: Particle SWARM algorithm Support VECTOR machine SVR ICE REGIME FORECAST China
• 年，卷（期）: 2012,（11）
• 页码范围: 36-40
• 页数: 5页
• 分类号: P338.4

摘要

[Objective] The research aimed to study forecast models for frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River based on SVR optimized by particle swarm optimization algorithm. [Method] Correlation analysis and cause analysis were used to select suitable forecast factor combination of the ice regime. Particle swarm optimization algorithm was used to determine the optimal parameter to construct forecast model. The model was used to forecast frozen and melted dates of the river water in Ningxia-Inner Mongolia section of the Yellow River. [Result] The model had high prediction accuracy and short running time. Average forecast error was 3.51 d, and average running time was 10.464 s. Its forecast effect was better than that of the support vector regression optimized by genetic algorithm (GA) and back propagation type neural network (BPNN). It could accurately forecast frozen and melted dates of the river water. [Conclusion] SVR based on particle swarm optimization algorithm could be used for ice regime forecast.