Snowfall and rainfall forecasting from the images of weather radar with artificial neural networks

We discuss problems of the weather forecasting technique with artificial neural networks and describe some solutions. We show that the computational time for learning with an acceleration learning algorithm can be reduced about 10 percent. To overcome the problem of overtraining, the pruning method is introduced and the prediction error is decreased about 20 percent. Using the data obtained over a winter, the prediction error with the neural technique is reduced about 60 percent than that with the cross correlation method