长江科学院院报2017，Vol.34Issue(12)：28-32,5.DOI:10.11988/ckyyb.20160837
粒子群算法优化BP在降雨空间插值中的应用
Application of BP Neural Network Optimized by Particle Swarm Optimization to Rainfall Spatial Interpolation
摘要
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.关键词
粒子群算法/BP神经网络/优化/克里金插值/降雨插值Key words
particle swarm optimization/BP neural network/optimization/Kriging interpolation/rainfall interpolation分类
天文与地球科学引用本文复制引用
邱云翔,张潇潇,刘国东..粒子群算法优化BP在降雨空间插值中的应用[J].长江科学院院报,2017,34(12):28-32,5.
