粒子群优化的神经网络在夏季降水预测中的应用

降水短期气候预测是一个非常复杂、重要的研究课题。为了提高其预测能力,拟采用1959—2011年逐月74项大气环流特征量序列、月平均500hPa高度场和月平均海温场,选取预测因子;用主分量分析方法提取样本数据中主要信息为综合因子。用粒子群优化人工神经网络方法,建立宣城市夏季降水短期气候预测模型。对2007—2011年宣城市夏季降水预报检验结果表明,粒子群优化人工神经网络收敛速度快,迭代次数少;试报平均绝对误差是66.5mm,绝对值平均相对误差10.5%,预测精度高,具有很好的应用推广前景。

Application of Particle-Swarm-Optimization to Prediction of Summer Rainfall Based Neural Networkice.

Precipitation of short-term climate prediction is a very complex and important research topic. In order to improve the predictive capability, particle-swarm-optimization based neural network reasoning models were established for Xuancheng, Anhui with summer precipitation prediction. The monthly data of 74 circumfluent eigen values, the monthly data of sea surface temperature, the monthly data of 500 hPa height from 1959 to 2011 to choose forecast factor, and principal component analysis method to extract the main information in the sample data for the comprehensive factor. 2007-2011 Xuancheng summer precipitation forecast verification results showed, particle swarm optimization artificial neural network convergence speed was fast, decrease in the number of iterations, and predicted the average absolute error was 66.5 mm, the absolute value of the average relative error of 10.5%, accurate prediction and had a good application prospect.