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<p>Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a significant challenge. This study employed machine learning (ML) techniques to model heavy metal pollution in soils within this critical ecosystem. A total of 91 standardized soil samples were analyzed to predict the accumulation of eight heavy metals using four distinct ML algorithms. Among them, random forest model outer performed in predicting As (0.79), Cd (0.89), Cr (0.63), Ni (0.56), Cu (0.60), and Zn (0.52), achieving notable R squared values. The feature attribute analysis identified As–K, Pb–K, Cd–S, Zn–Fe<sub>2</sub>O<sub>3</sub>, Cr– Fe<sub>2</sub>O<sub>3</sub>, Ni–Al<sub>2</sub>O<sub>3</sub>, Cu–P, and Mn– Fe<sub>2</sub>O<sub>3</sub> relationships resembled with correlation coefficients among them. The developed models revealed that the contamination factor for metals in soils indicated extremely high levels of Pb contamination (CF ≥ 6). In conclusion, this research offers a robust framework for predicting heavy metal pollution in coal mining soils, highlighting critical areas that require immediate conservation efforts. These findings emphasize the necessity for targeted environmental management and mitigation to reduce heavy metal pollution in mining sites.</p>

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Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh

  • 期刊论文

  • 发表日期：

    2025年4月23日

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  • Ram Proshad,
  • Krishno Chandra,
  • Maksudul Islam,
  • Dil Khurram,
  • Md Abdur Rahim,
  • Maksudur Rahman Asif,
  • Abubakr M. Idris

摘要

Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a significant challenge. This study employed machine learning (ML) techniques to model heavy metal pollution in soils within this critical ecosystem. A total of 91 standardized soil samples were analyzed to predict the accumulation of eight heavy metals using four distinct ML algorithms. Among them, random forest model outer performed in predicting As (0.79), Cd (0.89), Cr (0.63), Ni (0.56), Cu (0.60), and Zn (0.52), achieving notable R squared values. The feature attribute analysis identified As–K, Pb–K, Cd–S, Zn–Fe2O3, Cr– Fe2O3, Ni–Al2O3, Cu–P, and Mn– Fe2O3 relationships resembled with correlation coefficients among them. The developed models revealed that the contamination factor for metals in soils indicated extremely high levels of Pb contamination (CF ≥ 6). In conclusion, this research offers a robust framework for predicting heavy metal pollution in coal mining soils, highlighting critical areas that require immediate conservation efforts. These findings emphasize the necessity for targeted environmental management and mitigation to reduce heavy metal pollution in mining sites.