计算机科学 ›› 2015, Vol. 42 ›› Issue (Z6): 138-142.

• 智能计算 • 上一篇    下一篇

月降水量预测的粒子群-小波神经网络模型

龙云,贺新光,章新平   

  1. 湖南师范大学资源与环境科学学院 长沙410081,湖南师范大学资源与环境科学学院 长沙410081,湖南师范大学资源与环境科学学院 长沙410081
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金项目(41272271,41171035),湖南省“十二五”重点学科地理学和湖南省教育厅科研项目(14A097)资助

Particle Swarm Optimized Wavelet Neural Network Models for Forecasting Monthly Precipitation

LONG Yun, HE Xin-guang and ZHANG Xin-ping   

  • Online:2018-11-14 Published:2018-11-14

摘要: 为了提高月降水量预测精度和处理神经网络隐藏层神经元个数优化问题,提出了一种基于粒子群优化的小波多神经网络模型,并将其应用于洞庭湖流域月降水量的预测。首先,将大尺度气候指数和标准化月降水量作为预测因子在不同时间尺度上分解,然后使用多个基于粒子群算法以确定各隐藏层神经元个数的cascade-forward(CF)神经网络,用以对各频率下的标准月降水量子序列分别进行预测,最后通过重构和逆标准化得到月降水量预测值。结果表明:基于粒子群优化的小波多神经网络的预测精度高于小波单神经网络的预测精度,并且对极端月降水量的预测也有所改善。

Abstract: To improve the forecasting accuracy of monthly precipitation and deal with the determination of the number of hidden neurons in neural networks,this paper introduced a particle swarm optimized wavelet multiple neural network model,which was applied to the prediction of monthly precipitation in Dongting Lake Basin.The standardized monthly precipitation and large-scale climate index time series were first decomposed at different temporal scales as predictors.Then the standardized monthly precipitation subseries were forecasted,respectively,under different time scales by using the cascade-forward(CF) neural networks in which the number of hidden neurons is optimized by particle swarm optimization(PSO).Finally the monthly precipitation was forecasted by combining all predicted subseries and using the inverse transform of standardized monthly precipitation.The results show that PSO-based wavelet multiple neural network model provides more accurate forecasts than wavelet single neural network model for monthly precipitation in Dongting Lake Basin and improves the prediction accuracy of extreme monthly rainfall.

Key words: Wavelet neural networks,Particle swarm optimization,Dongting Lake Basin,Monthly rainfall prediction

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