Analysis of runoff generation driving factors based on hydrological model and interpretable machine learning method

Abstract

Xiaoqing River Basin, Shandong Province, China Identifying the driving factors of temporal and spatial variation in runoff is key to water resource management. The traditional machine learning model lacks transparency and interpretability, which affects the wide application of machine learning in the identification of influencing factors of hydrology. Interpretable machine learning method can improve the interpretability of machine learning model. The extreme gradient boosting (XGBoost) is established based on the data generated by the calibrated Soil Water Assessment Tool (SWAT), and the XGBoost is interpreted using the Shapely additive explanations (SHAP) method to identify the impact of driving factors on runoff generation. The results show that XGBoost can simulate the simulation ability of SWAT, and SHAP can identify the factors affecting runoff generation by interpreting XGBoost. It was found that climatic features have different effects on runoff in different sub-basins, and rainfall at high elevations (or slope) has stronger effects on runoff than that at low elevations. There is an obvious threshold effect of land use combination (or slope) on the generation of runoff, and this threshold effect is driven by high precipitation. The results of this study can provide a new method for factor analysis of runoff.