TY - JOUR
T1 - An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines
AU - Luan, Boyu
AU - Zhou, Wei
AU - Jiskani, Izhar Mithal
AU - Wang, Zhiming
N1 - Funding Information:
This research was supported by the Independent Research Project of the State Key Laboratory of Coal Resources and Safe Mining, CUMT (SKLCRSM001).
Publisher Copyright:
© 2023 by the authors.
PY - 2023/1/11
Y1 - 2023/1/11
N2 - 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/m3 for PM2.5, from 15.73 to 5.28 μg/m3 for PM10, and from 18.99 to 6.27 μg/m3 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.
AB - 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/m3 for PM2.5, from 15.73 to 5.28 μg/m3 for PM10, and from 18.99 to 6.27 μg/m3 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.
KW - dust pollution
KW - estimation
KW - Markov chains
KW - open-pit coal mine
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85146776545&partnerID=8YFLogxK
U2 - 10.3390/ijerph20021353
DO - 10.3390/ijerph20021353
M3 - Journal article
C2 - 36674111
AN - SCOPUS:85146776545
SN - 1661-7827
VL - 20
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 2
M1 - 1353
ER -