Identification model of mine water inrush source based on XGBoost and SHAP.

This study focuses on the construction and interpretation of a mine water inrush source identification model to enhance the precision and credibility of the model. For water inrush source identification and feature analysis, a novel method combining XGBoost and SHAP is suggested. The model uses Ca, Mg, K + Na, HCO, Cl, SO, Hardness, and pH as discriminators, and the key parameters in the XGBoost model are optimized by introducing the improved sparrow search algorithm. The Sparrow Search Algorithm combines Tent chaos mapping and Levy flight strategy (CLSSA), which makes the optimization process better balance the global search ability and local search ability, so as to improve the efficiency and effect of parameter optimization. Specifically, CLSSA is used to optimize key parameters of XGBoost, including the number of weak estimators (NE), tree depth (TD), model learning rate (LR), and then establishes a mine water inrush source identification model based on the CLSSA-XGBoost. Moreover, the model combines SHAP explainable framework to analyze key features of the identification results and interpret the impact of these features. Verified by 160 sample sets in Xinzhuangzi Mine, the average prediction precision of the CLSSA-XGBoost is 97.78%, the average prediction recall rate is 97.59% and the F1 is 97.61%, which are better than other comparison models. The SHAP provides global and local predictive explanatory analysis, revealing key factors for identifying different water inrush sources, enhancing the credibility of prediction results, and helping mine safety personnel make accurate decisions.