人工神经网络在月降水量预测业务中的研究和应用综述

月降水量的年际变化具有显著的非线性变化特征,预测难度大,历来是重大气象灾害预测的重点难点问题。BP(back propagation)神经网络在月降水量预测业务中的研究和应用中,取得了较好的成果,其中应用较广泛的是PCA-BP神经网络模型、遗传算法优化神经网络、RBF神经网络预测模型、小波神经网络模型、粒子群-神经网络模型等,这些方法也在广西月降水量预测业务中得到很好的应用,对提高月降水量预测能力有较大帮助。因此,有必要对目前神经网络在月降水量预测中的优势和不足进行综述,提出未来研究需要关注的重点关键问题。

Research and application of artificial neural network in monthly precipitation forecast

The interannual variation of monthly precipitation is characterized by significant nonlinear variation,which is difficult to predict and has always been a key and difficult problem in the prediction of major meteorological disasters.BP neural network has achieved good results in the research and application of monthly precipitation forecast.PCA-BP Neural network model,genetic algorithm optimization neural network,RBF neural network prediction model,wavelet neural network model,and particle swarm optimization neural network model are widely used.These models are also applied well in Guangxi monthly precipitation prediction business,which is helpful to improve the monthly precipitation prediction ability.Therefore,it is necessary to summarize the advantages and disadvantages of neural network in monthly precipitation prediction,and put forward the key issues for future research.