Urban Groundwater Level Based On Bp Neural Network Prediction

Groundwater level variation in city has been quantitatively predicted by using BP networks of artificial neural networks in this paper. The network consists of three layers: the input layer, the hidden layer, and the output layer. The nodes and the connectivity between the layers determine the performance of the BP neural network. Also discussed are the problem of determining the number of hidden layer's neural nodes, the problem of normalization of the input vector, and the problem of initialization of connection weights. etc. A predictive system of groundwater level variation based on the BP neural network is programmed with C language and constructed in the operation system of Win98. Some application program modules all called by means of file. The experimental result shows that compared with the results obtained by linear regressive model, the BP neural network is higher than that of linear regressive model and is an effective predictive method.