关键词: 粒子群算法, BP神经网络, 优化, 克里金插值, 降雨插值

Abstract: To better describe the spatial distribution of rainfall, we applied BP neural network optimized by particle swarm optimization to the daily, monthly and yearly rainfall spatial interpolation of the Three Gorges reservoir area, and compared the performance with those of simple BP and Kriging interpolation. We found that in daily and yearly time-scale, PSO-BP neural network performs better than BP and Kriging; while in terms of monthly time-cale, PSO-BP result is close to BP and better than Kriging. We conclude that BP neural network optimized by particle swarm optimization could better reveal the law of spatial distribution of rainfall and has the ability of spatial interpolation in different timescales, and therefore is an excellent method for rainfall spatial interpolation.

Key words: particle swarm optimization, BP neural network, optimization, Kriging interpolation, rainfall interpolation