中文摘要：

在西南中国的 Karstic 含水土层大部分位于多山的区域，地下水水平观察数据通常是不在的。因此，数字地下水模型为地下水流动和降雨地下流出回答的模拟是不恰当的。在这研究，一个人工的神经网络(ANN ) 模型被开发模仿地下的溪流分泌物。ANN 模型在西南中国的贵州省被用于 Houzhai 地下的排水，它在中国的潮湿的区域的 karstic 地形学是代表性的。在每日的降雨和流出系列之间的关联分析被用来决定模型输入，时间落后。ANN 模型用一个错误 backpropagation 算法被训练并且与不同 karstic 特征在三个水文学车站验证了。学习结果证明 ANN 模型在当模特儿高度非线性的 karstic 含水土层表演很好。

英文摘要：

Karstic aquifers in Southwest China are largely located in mountainous areas and groundwater level observation data are usually absent. Therefore, numerical groundwater models are inappropriate for simulation of groundwater flow and rainfall-underground outflow responses. In this study, an artificial neural network （ANN） model was developed to simulate underground stream discharge. The ANN model was applied to the Houzhai subterranean drainage in Guizhou Province of Southwest China, which is representative of karstic geomorphology in the humid areas of China. Correlation analysis between daily rainfall and the outflow series was used to determine the model inputs and time lags. The ANN model was trained using an error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well in the modeling of highly non-linear karstic aquifers.