Application of BP Neural Network Optimized by Particle SwarmOptimization to Rainfall Spatial Interpolation

QIU  Yun-xiang; ZHANG  Xiao-xiao; LIU Guo-dong

doi:10.11988/ckyyb.20160837

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Journal of Changjiang River Scientific Research Institute ›› 2017, Vol. 34 ›› Issue (12) : 28-32. DOI: 10.11988/ckyyb.20160837

WATER RESOURCES AND ENVIRONMENT

Application of BP Neural Network Optimized by Particle SwarmOptimization to Rainfall Spatial Interpolation

QIU Yun-xiang¹, ZHANG Xiao-xiao¹, LIU Guo-dong^{1, 2}

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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.

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particle swarm optimization / BP neural network / optimization / Kriging interpolation / rainfall interpolation

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QIU Yun-xiang, ZHANG Xiao-xiao, LIU Guo-dong. Application of BP Neural Network Optimized by Particle SwarmOptimization to Rainfall Spatial Interpolation[J]. Journal of Changjiang River Scientific Research Institute. 2017, 34(12): 28-32 https://doi.org/10.11988/ckyyb.20160837

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