Simulation of Rainfall-Underground Outflow Responses of a Karstic Watershed in Southwest China with an Artificial Neural Network

In the karstic system, numerous karstic features influence hydrologic behavior. Underground stream discharges reflect the fluctuation of ground water level and variability of ground water storage in the watersheds. However, karstic aquifers in southwest China are largely located in the mountain areas and observation data of groundwater level 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 flow discharges. ANN model was applied in the Houzhai subterranean drainage watershed in Guizhou Province of southwest China, a representative of karstic geomorphology in the humid areas of China. Correlation was used to determine the model inputs and time-lags between inputs and outputs. ANN model was trained using the error backpropagation algorithm and validated at three hydrological stations with different karstic features. Study results show that the ANN model performs well for the modeling of karstic aquifers, which are highly non-linear systems. The ANN model offers a promising tool for better understanding of karstic hydrological processes and thus estimation of groundwater resources in the karstic aquifer.