Xu Jin, Zheng Lulin, Lan Hong, Zuo Yujun, Li Bo, Tian Shiyu, Tian Youwen
College of Mining, Guizhou University, Guiyang, 550025, PR China.
College of Resources and Environmental Engineering, Guizhou University, Guiyang, 550025, PR China.
Sci Rep. 2024 Oct 9;14(1):23508. doi: 10.1038/s41598-024-74417-1.
Accurate and efficient identification of water inrush sources, as one of the three critical elements of mine water hazards, is crucial for mine water management. To identify the sources of mine water inrush effectively, a model named HBA-CatBoost is introduced. This model is established on the hybrid bat algorithm (HBA)-optimized category feature gradient boosting tree (CatBoost), and the shapley additive explanation (SHAP) method is employed to elucidate the model's decision-making process. Given the prevalent occurrence of water hazards in coal seam roofs and floors in the northern Guizhou coalfield, coupled with the challenges in pinpointing water inrush sources in mines, the HBA-CatBoost model is tested at the Longfeng Coal Mine in northern Guizhou to validate its practicality. Comparative analysis with the HBA-RF, HBA-XGBoost, CatBoost, RF, and XGBoost models demonstrates that the hybrid bat algorithm significantly enhances the classification performance of the CatBoost model, resulting in improved convergence speed and classification accuracy. The HBA-CatBoost model outperforms the aforementioned models in terms of classification effectiveness, achieving accuracy, recall, precision, and F1 scores of 96.43%, 97.22%, 96.43%, and 96.61%, respectively. The SHAP method elucidates the decision mechanism of the optimal HBA-CatBoost model, highlighting the significance of sample features and bolstering the model's credibility. These outcomes underscore the superior performance of the HBA-CatBoost model and its potential for effectively identifying water inrush sources in mines.
准确高效地识别突水水源作为矿井水害三大关键要素之一，对矿井水害治理至关重要。为有效识别矿井突水水源，引入了一种名为HBA-CatBoost的模型。该模型基于混合蝙蝠算法（HBA）优化的类别特征梯度提升树（CatBoost）建立，并采用夏普利值附加解释（SHAP）方法来阐释模型的决策过程。鉴于贵州北部煤田煤层顶底板水害频发，且矿井突水水源定位存在挑战，在贵州北部的龙凤煤矿对HBA-CatBoost模型进行了测试，以验证其实用性。与HBA-RF、HBA-XGBoost、CatBoost、RF和XGBoost模型的对比分析表明，混合蝙蝠算法显著提升了CatBoost模型的分类性能，提高了收敛速度和分类精度。HBA-CatBoost模型在分类效果方面优于上述模型，准确率、召回率、精确率和F1分数分别达到96.43%、97.22%、96.43%和96.61%。SHAP方法阐释了最优HBA-CatBoost模型的决策机制，突出了样本特征的重要性，增强了模型的可信度。这些结果凸显了HBA-CatBoost模型的卓越性能及其在有效识别矿井突水水源方面的潜力。
