BP神经网络预测河流月径流量

河流的月径流量是随机变化的，影响因素很多，如人类活动、降雨、下垫面的土壤、植被覆盖情况。利用人工神经网络理论建立BP（Back-Propagation，反向传播方法）网络预测模型，用该模型对河流的月径流量进行预测，BP神经网络模型计算快速，占用内存小，还有很好的容错性，可以得到比较理想的结果，精度高，可靠性好。模型建立之后，将其用于实例，通过对大量样本进行很多次的训练学习，得到训练好的BP网络模型，最后进行预测，得到令人比较满意的结果。

Prediction of monthly runoff based on BP neural network

The monthly runoff changes randomly and there are a lot of influence ingredients,such as human activities,rainfall,the soil and plant conditions on the ground.BP neural network based on the theory of artificial neural network is set up and this BP neural network is used to predict the every-month runoff,whereas the BP neural network model can give the result quickly,and it's precise and credible.We use the model to predict.First,use a large mount of samples to practice the BP neural network medel.After such training,this model can be utilized to predict the monthly runoff,and a satisfied result will be got finally.