Evaluation Research of Groundwater Resources Based on Artificial Neural Networks in the Sanjiang Plain

Replacing Least Squares Method by Real coding based Accelerating Genetic Algorithm (RAGA), the parameters of time response function in the GM (1, 1) Model are optimized. Combined with BP Artificial Neural Networks Model, the Equa ldimension Gray Filling BP Neural Networks Model Based on RAGA is established. By this model, predicted the groundwater depth of Chuangye farm in Sanjiang Plain. The structural of BP Neural Networks is 3 : 12 : 3. The relative error is only 2.33%. Comparing with the traditional GM (1, 1) Model or BP Neural Networks Model, the precision is highly increased. The result shows that the groundwater deep will descend 0.3m in average annually in the area from 2007 to 2012.