Simulation of rainfall-underground outflow responses of a karstic watershed in Southwest China with an artificial neural network  

作　　者：Chen Xi Chen Cai Hao Qingqing Zhang Zhicai Shi Peng 

机构地区：[1]State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing210098, P. R. China

出　　处：《Water Science and Engineering》2008年第2期1-9,共9页水科学与水工程（英文版）

基　　金：supported by the National Basic Research Program of China (973 Program, Grant No 2006CB403200);the National Natural Scientific Foundation of China (Grant No 50679025);the 111 Project of the Ministry of Education and the State Administration of Foreign Expert Affairs, China (Grant No. B08048)

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.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.

关 键 词：KARST underground channel correlation analysis artificial neural network 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] P617[自动化与计算机技术—控制科学与工程]