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<p>To effectively manage seismic risks in mining activities, especially underground mining, and reduce disaster occurrences and casualties. This article compares advanced machine learning strategies including Decision Tree Classification (DTC), Seahorse Optimizer (SHO), Northern Hawk Optimization (NGO), and Sunflower Optimization Framework (SOA) for improving risk prediction models. The study evaluated the effectiveness of these methods by comparing the performance evaluation indicators of different models, such as precision, recall, and F1 scores. The results show that the DTC-based model combined with optimization technologies such as SHO, NGO and SOA achieved high accuracy in both the training and testing phases. Among them, the DTSH model achieved an accuracy of 0.984 on the training set and also achieved good results on the test set. performance, significantly improving the ability to predict earthquake disasters in coal mining.</p>

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Seismic hazard prediction in mining activities using metaheuristic algorithms

  • 期刊论文

  • 发表日期：

    2025年2月7日

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  • Jie Cai,
  • Chunbo Fang,
  • Juncheng Li,
  • Mingang Li,
  • Sheng Chen,
  • Jinwen Zhou

摘要

To effectively manage seismic risks in mining activities, especially underground mining, and reduce disaster occurrences and casualties. This article compares advanced machine learning strategies including Decision Tree Classification (DTC), Seahorse Optimizer (SHO), Northern Hawk Optimization (NGO), and Sunflower Optimization Framework (SOA) for improving risk prediction models. The study evaluated the effectiveness of these methods by comparing the performance evaluation indicators of different models, such as precision, recall, and F1 scores. The results show that the DTC-based model combined with optimization technologies such as SHO, NGO and SOA achieved high accuracy in both the training and testing phases. Among them, the DTSH model achieved an accuracy of 0.984 on the training set and also achieved good results on the test set. performance, significantly improving the ability to predict earthquake disasters in coal mining.