• 责任者：Su Anyu (School of Geographic and Oceanographic Science ; Nanjing University ; Nanjing 210093 ; China) ; Li Heng
• 刊名：Scientia Geographica Sinica
• 出版年：2009
• 卷期：29(2)
• 关键词：BP神经网络系统 ; BP neural network system ; 地下水资源 ; groundwater resources ; 三江平原 ; Sanjiang Plain
• 页码：p.283-287
• 图表：1 illus., 4 tables, 8 refs.
• 分类号：1701
• issnNo：ISSN1000-0690
• isbnNo：CN22-1124/P

Replacing Least Square Method by Real coding based Accelerating Genetic Algorithm, the parameters of time response function in the GM (1,1) Model are optimized. Combined with BP Artificial Neural Networks Model, the Equal-dimension Gray Filling BP Neural Networks Model Based on RAGA is established. By this model, predicted the groundwater depth of Chuangye Farm in the 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 0.3 m in average annually in the area.