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Land subsidence prediction in coal mining using machine learning models and optimization techniques.

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Accurate land subsidence prediction in coal mining is improved using a novel Gene Expression Programming (GEP) model. Optimized with the Artificial Bee Colony (ABC) algorithm, this method enhances accuracy for predicting surface deformation from underground resource extraction.

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Area of Science:

  • Geotechnical Engineering
  • Environmental Science
  • Computational Intelligence

Background:

  • Land subsidence is a significant environmental hazard caused by underground resource extraction.
  • Accurate prediction of subsidence is vital for mitigating damage to surface structures in mining areas.
  • Existing models often lack the comprehensive parameter consideration needed for precise subsidence estimation.

Purpose of the Study:

  • To develop and validate an improved prediction model for land subsidence in coal mining, specifically using the longwall method.
  • To enhance the accuracy of subsidence prediction by incorporating a wide range of geological and mining parameters.
  • To investigate the influence of various parameters on subsidence using advanced computational techniques.

Main Methods:

  • Utilized Gene Expression Programming (GEP) for subsidence prediction modeling.
  • Optimized the GEP algorithm using a hybrid approach combining the Artificial Bee Colony (ABC) and Ant Lion Optimizer (ALO) algorithms.
  • Collected and analyzed data from 14 coal mines, considering 11 key parameters related to mining and overburden properties.

Main Results:

  • The GEP model optimized with the ABC algorithm achieved the highest prediction accuracy, with a correlation coefficient of 0.96.
  • Sensitivity analysis indicated that mining depth has the most significant impact on land subsidence.
  • Overburden density was found to have the least effect on subsidence prediction in the studied coal mines.

Conclusions:

  • The hybrid GEP-ABC model offers a highly accurate and reliable method for predicting land subsidence in longwall coal mining.
  • The study highlights the critical role of mining depth and overburden properties in the subsidence phenomenon.
  • This research provides a valuable tool for risk assessment and management in mining regions susceptible to subsidence.