本案例是采用GRNN（径向基）神经网络预测水资源量，GRNN神经网络适合于小样本、高精度预测，本实例附有数据（不全），下载者可以直接将数据更改为自己的数据便可以使用。程序中使用了交叉验证方法，使预测精度大大提高-This case is the use of GRNN (Radial Basis Function) neural network to predict the amount of water, GRNN neural networks suitable for small samples, high-precision prediction, the present example with data (incomplete), can directly download data to their own data will change can use. Procedures used in the cross-validation method, the prediction accuracy is improved greatly