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

Boyu Luan, Wei Zhou (), Izhar Mithal Jiskani and Zhiming Wang
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Boyu Luan: State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
Wei Zhou: State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China
Izhar Mithal Jiskani: Department of Mining Engineering, National University of Sciences & Technology, Balochistan Campus, Quetta 87300, Pakistan
Zhiming Wang: State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, China

IJERPH, 2023, vol. 20, issue 2, 1-16

Abstract: Dust is a severe environmental issue in open-pit mines, and accurate estimation of its concentration allows for viable solutions for its control and management. This research proposes a machine learning-based solution for accurately estimating dust concentrations. The proposed approach, tested using real data from the Haerwusu open-pit coal mine in China, is based upon the integrated random forest-Markov chain (RF-MC) model. The random forest method is used for estimation, while the Markov chain is used for estimation correction. The wind speed, temperature, humidity, and atmospheric pressure are used as inputs, while PM2.5, PM10, and TSP are taken as estimated outputs. A detailed procedure for implementing the RF-MC is presented, and the estimated performance is analyzed. The results show that after correction, the root mean squared error significantly decreased from 7.40 to 2.56 μg/m 3 for PM2.5, from 15.73 to 5.28 μg/m 3 for PM10, and from 18.99 to 6.27 μg/m 3 for TSP, and the Pearson correlation coefficient and the mean absolute error also improved considerably. This work provides an improved machine learning approach for dust concentration estimation in open-pit coal mines, with a greater emphasis on simplicity and rapid model updates, which is more applicable to ensure the prudent use of water resources and overall environmental conservation, both of which are advantageous to green mining.

Keywords: dust pollution; open-pit coal mine; estimation; random forest; Markov chains (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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