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Rapid identification model of mine water inrush source using random forest optimized by multi-strategy improved

Jierui Ling1, Zhibo Fu1, Kailong Xue1

  • 1School of Coal Engineering, Shanxi Datong University Datong 037000, China.

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This study introduces an improved Sparrow Search Algorithm (SSA) to optimize the Random Forest model for accurately identifying mine water inrush sources, enhancing coal mine safety.

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Discriminant modelImproved SSAKernel principal component analysisMine water inrushRFWater source identification

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Area of Science:

  • Geosciences
  • Mining Engineering
  • Artificial Intelligence

Background:

  • Mine water inrush accidents pose significant threats to coal mining operations.
  • Accurate identification of water inrush sources is crucial for preventing disasters.

Purpose of the Study:

  • To develop a fast and accurate method for identifying mine water inrush sources.
  • To enhance the performance of the Random Forest algorithm using an improved optimization technique.

Main Methods:

  • Kernel Principal Component Analysis (KPCA) was used to extract key factors from mine data.
  • An Improved Sparrow Search Algorithm (ISSA) was developed and validated.
  • The ISSA was employed to optimize hyperparameters of the Random Forest (RF) model.

Main Results:

  • The ISSA-optimized RF model demonstrated superior predictive performance over other models.
  • Application to a mine in Shandong province showed high accuracy, precision, recall, and F1 index.
  • The proposed method confirmed reliability and stability in identifying water inrush sources.

Conclusions:

  • The KPCA-based, ISSA-optimized RF model provides a robust and accurate approach for mine water inrush source identification.
  • This method enhances safety by offering a reliable guarantee for mining production.
  • The study validates the effectiveness of integrating advanced AI algorithms into mining safety protocols.