Water Science and Engineering 2008, 1(2) 1-9  DOI:   10.3882/j.issn.1674-2370.2008.02.001   ISSN: 1674-2370 CN: 32-1785/TV

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underground channel
correlation analysis
artificial neural network
Chen Xi
Chen Cai
Hao Qingqing
Zhang Zhicai
Shi Peng
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Simulation of rainfall-underground outflow responses of a karstic watershed in Southwest China with an artificial neural network

Chen Xi*, Chen Cai, Hao Qingqing, Zhang Zhicai, Shi Peng

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, P. R. China


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.

Keywords karst   underground channel   correlation analysis   artificial neural network  
Received 2008-04-24 Revised 2008-06-01 Online:  
DOI: 10.3882/j.issn.1674-2370.2008.02.001
Corresponding Authors: Chen Xi
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