Using explainable artificial intelligence (XAI) methods to understand the nonlinear relationship between the Three Gorges Dam and downstream flood

Abstract

Study Region: In the Yangtze River basin of China. Study focus: The emerging Explainable Artificial Intelligence (XAI) methods provide us an opportunity to understand the nonlinear relationship that the Deep Learning(DL) model learned inside. The construction of the Three Gorges Dam (TGD) has successfully minimized the likelihood of flooding in the Yangtze River basin. The XAI methods can help us to know the nonlinear relationship behind it. We apply the Long Short Term Memory (LSTM) network, in conjunction with two XAI methods, SHapley Additive exPlanation (SHAP) and Expected Gradient (EG), to do our work.In our DL model, we use YiChang (YC) station runoff,Precipitation (Pre) and vapour pressure deficit (VPD) data from the middle and lower river basin as input, while the output of the model generates runoff data at the DaTong (DT) station, XAI methods enable us to calculate the significance of each input feature is for generating the output feature in a DL model. In this study, we examine the difference in importance scores between the Before Three Gorges Dam (BTGD) period and the After Three Gorges Dam (ATGD) period. New Hydrological Insights for the Region: In the BTGD period, YC runoff was the primary contributor to flooding at the DT station. However, in the ATGD period, the largest contribution to flooding in the middle and lower Yangtze River basin has shifted from YC runoff to the the middle and lower reaches of precipitation. Our results suggest that the XAI can show the nonlinear relationship between the TGD and downstream flood clearly and the TGD can effectively mitigate flooding in the middle and lower river basins by regulating runoff from the upper river basin. The work shows the potential of XAI to explain the nonlinear relationship in the hydrology field.

Keywords

• Nonlinear relationship
• Runoff prediction
• LSTM
• Flood
• XAI
• TGD