A novel integrated framework for enhanced water source identification

Accurate identification of water sources is crucial for effective water management and safety in mining operations. However, imbalanced water sample datasets often lead to suboptimal classification accuracy. To address this challenge, this study proposes a novel water source identification method integrating Synthetic Minority Over-Sampling Technique (SMOTE), Zebra Optimization Algorithm (ZOA), and Light Gradient Boosting Machine (LightGBM). Initially, SMOTE is utilized to synthesize samples for the minority class within the imbalanced dataset, thereby generating a balanced water sample dataset and mitigating class distribution disparities. Subsequently, an efficient water source identification model is constructed by combining ZOA with LightGBM, leveraging the strengths of both algorithms. The model’s performance is validated using a test set and compared with other common classification models. Results demonstrate that SMOTE significantly alleviates class imbalance and enhances the classification accuracy of LightGBM for minority class water samples. ZOA parameter tuning accelerates model convergence and further improves classification accuracy, optimizing the model’s overall performance. In experimental validation, the proposed SMOTE-ZOA-LightGBM model achieved an accuracy of 88.41% and a F1 score of 88.24%, outperforming six other classification models. The method proposed in this paper can accurately identify water source types, effectively addressing the issue of low classification accuracy caused by imbalanced water sample data. It provides reliable technical support and scientific basis for identifying and preventing water inrush sources in mines.