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<p>The accurate identification of mine water inrush sources is crucial for quickly discovering water inrush points and preventing accidents. This study focuses on the&#xa0;Xinshanghai No. 1 coal mine, where&#xa0;52 sets of water sample data&#xa0;were collected and preprocessed using the&#xa0;SMOTE algorithm&#xa0;to address data imbalances. To enhance identification accuracy, an&#xa0;improved whale optimization algorithm-optimized support vector machine (IWOA-SVM) model&#xa0;was developed. Then, a comparative analysis was conducted using the IWOA-SVM, WOA-SVM, and SVM models. IWOA-SVM attained 93.75% accuracy, outperforming WOA-SVM and SVM by 6.25% and 18.75%, respectively. In addition, practical validation using actual water inrush data from the Xinshanghai No. 1 coal mine demonstrated perfect agreement between the model identifications and actual water inrush sources. Therefore, the IWOA-SVM model demonstrates high accuracy and reliability in identifying mine water inrush sources, and can provide effective technical support and decision basis for coal mine water hazard prevention and control.</p>

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A Mine Water Inrush Source Identification Method Based on IWOA-SVM

  • 期刊论文

  • 发表日期：

    2025年11月24日

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  • Lianjing Ma,
  • Mengna Diwu,
  • Baofeng Zhao,
  • Yuguang Lv,
  • Yang Zhang,
  • Di Liu,
  • Song Jiang,
  • Caiwu Lu,
  • Qinghua Gu

摘要

The accurate identification of mine water inrush sources is crucial for quickly discovering water inrush points and preventing accidents. This study focuses on the Xinshanghai No. 1 coal mine, where 52 sets of water sample data were collected and preprocessed using the SMOTE algorithm to address data imbalances. To enhance identification accuracy, an improved whale optimization algorithm-optimized support vector machine (IWOA-SVM) model was developed. Then, a comparative analysis was conducted using the IWOA-SVM, WOA-SVM, and SVM models. IWOA-SVM attained 93.75% accuracy, outperforming WOA-SVM and SVM by 6.25% and 18.75%, respectively. In addition, practical validation using actual water inrush data from the Xinshanghai No. 1 coal mine demonstrated perfect agreement between the model identifications and actual water inrush sources. Therefore, the IWOA-SVM model demonstrates high accuracy and reliability in identifying mine water inrush sources, and can provide effective technical support and decision basis for coal mine water hazard prevention and control.