基于级联神经网络的年降雨量预测

降雨量是农业生产的一个重要影响因素,如何准确预测降雨量成为指导农业、水利等一项重要的科技指标。从信息利用角度来看,单一预测模型仅能利用降雨量数据部分有效信息,而组合模型将单一模型的优势互补,可获得更佳的预测效果。基于神经网络理论的快速发展及级联神经网络预测模型被广泛应用于各个方面并取得了很好的结果,针对降雨量曲线的特点,深入分析BP神经网络及RBF神经网络发现,BP神经网络可很好的拟合对降雨量有很大影响的气候信息和其它因素,输出同一类型的降雨量影响信息;RBF网络的特点就是可很好地提取同一类信息特征,二者的组合可很大程度的提高降雨量预测精度。鉴于此,将BP-RBF级联神经网络引入降雨量预测研究中,实例计算表明,该方法高于单一神经网络预测精度,证明该方法应用于降雨量预测是合理有效的。

Annual rainfall forecast based on cascade neural network

Rainfall is an important factor affecting agricultural production, how to forecast the rainfall become the guiding agriculture, water conservancy and other important indicators of science and technology. From the point of information utilization, the single forecasting model only use the part of the rainfall data, and the combination model will be complementary to the advantages of the single model, and get a better forecasting effect. The rapid development of neural network theory and Cascade neural network prediction model is widely used in all aspects and achieved good results. According to the characteristics of rainfall curve, Through the analysis of BP neural network and RBF neural network, we can find that BP neural network can be a good fit for the rainfall has a great impact on climate information and other factors, the output of the same type of rainfall impact information, and The characteristics of RBF network can be used to extract the features of the same kind of information, and the combination of the two can greatly improve the accuracy of rainfall prediction. In view of this, the BP-RBF cascade neural network is introduced into the study of rainfall prediction. The calculation results show that the proposed method is higher than the single neural network prediction accuracy, which proves that the method is reasonable and effective.