1 Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien street, Duc Thang ward, Bac Tu Liem district, Hanoi 100000, Vietnam; firstname.lastname@example.org (H.N.); email@example.com (Q.T.L.); firstname.lastname@example.org (T.Q.H.)
2 Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, 18 Vien street, Duc Thang ward, Bac Tu Liem district, Hanoi 100000, Vietnam.
*Corresponding author: email@example.com. Tel.: +84–989583095
In this paper, an artificial neural network (ANN) model was applied to forecast PM2.5 at the Coc Sau open–pit coal mine (Northern Vietnam) with fine–tuning parameters. It aims to provide the feasibility and insights into controlling air quality in open–pit mines using artificial intelligence techniques. Accordingly, an air quality monitoring system was established to monitor hourly PM2.5 datasets for more than three months. Subsequently, 80% of the whole data was used to design and tune the ANN model, and the remaining 20% was used for testing the PM2.5 predictions. An ANN model with a single hidden layer and ten nodes was developed for this aim. The stochastic gradient descent algorithm was applied to train the ANN model under the learning rate of 0.001 to avoid the overfitting of the model. In addition, 10 time steps (multi–step forecasting model) were applied to forecast the next time step. The results indicated that ANN is a potential model for forecasting PM2.5 in open–pit mines with high accuracy (RMSE = 2.000), and it can be used to control real–time air quality in open–pit mines.
Cite this paper
Nam, X.B.; Hoang, N.; Qui, T.L.; Hieu, T.Q. Application of artificial neural network with fine–tuning parameters for forecasting PM2.5 in deep open–pit mines: A case study. VN J. Hydrometeorol 2022, 10, 64-72.
1. Silvester, S.; Lowndes, I.; Hargreaves, D. A computational study of particulate emissions from an open pit quarry under neutral atmospheric conditions. Atmos. Environ. 2009, 43(40), 6415–6424.
2. Alvarado, M.; Gonzalez, F.; Fletcher, A.; Doshi, A. Towards the development of a low cost airborne sensing system to monitor dust particles after blasting at open–pit mine sites. Sensors 2015, 15(8), 19667–19687.
3. Ali, M.U.; Liu, G.; Yousaf, B.; Ullah, H.; Abbas, Q.; Munir, M.A.M. A systematic review on global pollution status of particulate matter–associated potential toxic elements and health perspectives in urban environment. Environ. Geochem. Health 2019, 41(3), 1131–1162.
4. Qi, C.; Zhou, W.; Lu, X.; Luo, H.; Pham, B.T.; Yaseen, Z.M. Particulate matter concentration from open–cut coal mines: A hybrid machine learning estimation. Environ. Pollut. 2020, 263, 114517.
5. Shou, Y.; Huang, Y.; Zhu, X.; Liu, C.; Hu, Y.; Wang, H. A review of the possible associations between ambient PM2. 5 exposures and the development of Alzheimer's disease. Ecotoxicol. Environ. Saf. 2019, 174, 344–352.
6. Lu, X.; Zhou, W.; Qi, C.; Luo, H.; Zhang, D.; Pham, B.T. Prediction into the future: A novel intelligent approach for PM2. 5 forecasting in the ambient air of open–pit mining. Atmos. Pollut. Res. 2021, 12(6), 101084.
7. Li, L.; Zhang, R.; Sun, J.; He, Q.; Kong, L.; Liu, X. Monitoring and prediction of dust concentration in an open–pit mine using a deep–learning algorithm. J. Environ. Health Sci. Eng. 2021, 19(1), 401–414.
8. Hyder, Z.; Siau, K.; Nah, F. Artificial intelligence, machine learning, and autonomous technologies in mining industry. J. Database Manage. 2019, 30(2), 67–79.
9. Ali, D.; Frimpong, S. Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector. Artif. Intell. Rev. 2020, 53(8), 6025–6042.
10. Soofastaei, A. The application of artificial intelligence to reduce greenhouse gas emissions in the mining industry. In Green Technologies to Improve the Environment on Earth: IntechOpen London, UK, 2018.
11. Prieto, A.; Prieto, B.; Ortigosa, E.M.; Ros, E.; Pelayo, F.; Ortega, J. et al. Neural networks: An overview of early research, current frameworks and new challenges. Neurocomputing 2016, 214, 242–268.
12. Zhu, Z.; Qiao, Y.; Liu, Q.; Lin, C.; Dang, E.; Fu, W. et al. The impact of meteorological conditions on Air Quality Index under different urbanization gradients: a case from Taipei. Environ. Dev. Sustainability 2021, 23(3), 3994–4010. doi:10.1007/s10668-020-00753-7.
13. Zhang, Y. Dynamic effect analysis of meteorological conditions on air pollution: A case study from Beijing. Sci. Total Environ. 2019, 684, 178–185.
14. He, J.; Gong, S.; Yu, Y.; Yu, L.; Wu, L.; Mao, H.; et al. Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities. Environ. Pollut. 2017, 223, 484–496.
15. Madhiarasan, M.; Deepa, S. Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting. Artif. Intell. Rev. 2017, 48(4), 449–471.
16. Niska, H.; Hiltunen, T.; Karppinen, A.; Ruuskanen, J.; Kolehmainen, M. Evolving the neural network model for forecasting air pollution time series. Eng. Appl. Artif. Intell. 2004, 17(2), 159–167.
17. Armstrong, J.S. Evaluating forecasting methods. In Principles of forecasting, Springer, 2001, pp. 443–472.