Study on the Precipitation Forecast Based on BP-Neural Network and Wavelet Analysis
Abstract: Jiangsu province is located in the Yangtze and Huaihe River basins, which is one of the areas most severely affected by droughts and floods. Therefore, it is important to study on the precipitation forecast in this area for the flood control with drought relief. In this paper, the precipitation forecast model based on BP Neural Network and Wavelet Neural Network is established with the precipitation data from four rainfall sta-tions in Jiangsu Province, which include the Xuzhou, Ganyu, Dongtai and Nanjing Stations. The following conclusions can be obtained by the example analysis: 1) The minimum relative error of BP neural network is 1.16%, the maximum relative error is 16.35%, the determine coefficient is 0.87, the mean square error is 4.27%; 2) The minimum relative error of WNN neural network is 0.7%, the maximum relative error is 88.65%, the determine coefficient is 0.94, the mean square error is 4.2%. The results show that: 1) It is feasi-ble to apply the back propagation neural network precipitation forecast model. To a certain extent, this model can reflect the trends of the precipitation; 2) There is more error of WNN in some years, so a variety of forecasting methods in practice are used to mutual authentication and mutual checking to improve forecast accuracy.
文章引用: 熊海晶 , 王式成 , 王栋 (2012) BP神经网络和小波分析在年降水预报中的应用研究。 水资源研究， 1， 340-346. doi: 10.12677/JWRR.2012.15052
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