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An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines.

Boyu Luan1,2, Wei Zhou1,2, Izhar Mithal Jiskani3

  • 1State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China.

International Journal of Environmental Research and Public Health
|January 21, 2023
PubMed
Summary

This study introduces an improved machine learning model for estimating dust concentrations in open-pit mines. The integrated random forest-Markov chain (RF-MC) model significantly enhances accuracy for PM2.5, PM10, and TSP measurements.

Keywords:
Markov chainsdust pollutionestimationopen-pit coal minerandom forest

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Area of Science:

  • Environmental Science
  • Data Science
  • Mining Engineering

Background:

  • Dust pollution is a critical environmental challenge in open-pit mining operations.
  • Accurate dust concentration estimation is essential for effective control and management strategies.

Purpose of the Study:

  • To develop and validate a novel machine learning approach for precise dust concentration estimation in open-pit coal mines.
  • To improve upon existing methods by integrating Random Forest and Markov Chain models.

Main Methods:

  • Utilized an integrated Random Forest-Markov Chain (RF-MC) model for dust concentration estimation.
  • Employed meteorological data (wind speed, temperature, humidity, atmospheric pressure) as input variables.
  • Focused on estimating particulate matter (PM2.5, PM10) and total suspended particulates (TSP) concentrations.

Main Results:

  • The RF-MC model demonstrated significant improvements in estimation accuracy after correction.
  • Root mean squared error (RMSE) decreased substantially for PM2.5 (7.40 to 2.56 μg/m³), PM10 (15.73 to 5.28 μg/m³), and TSP (18.99 to 6.27 μg/m³).
  • Pearson correlation coefficient and mean absolute error also showed considerable improvements, indicating higher model reliability.

Conclusions:

  • The proposed RF-MC model offers a simplified and rapidly updatable machine learning solution for dust estimation in open-pit mines.
  • This approach supports prudent water resource management and environmental conservation, contributing to green mining practices.