A Lasso and Elastic-Net Regularized Generalized Linear Model for Predicting Blast-Induced Air Over-pressure in Open-Pit Mines

• Autorzy: Bui Xuan‑Nam , Nguyen Hoang , Tran Quang Hieu , Bui Hoang‑Bac , Nguyen Quoc Long , Nguyen Dinh An , Le Thi Thu Hoa , Pham Van Viet

Identyfikatory

DOI

10.29227/IM-2019-02-52

Warianty tytułu

PL

Model Lasso i uogólniony model liniowy elastycznej siatki do prognozowania nadciśnienia wywołanego wybuchem w kopalniach odkrywkowych

• Konferencja: POL-VIET 2019 : scientific-research cooperation between Poland and Vietnam : 08–10.07.2019, Krakow
• Języki publikacji: EN

Abstrakty

EN

Air overpressure (AOp) is one of the products of blasting operations in open-pit mines which have a great impact on the environment and public health. It can be dangerous for the lungs, brain, hearing and the other human senses. In addition, the impact on the surrounding environment such as the vibration of buildings, break the glass door systems are also dangerous agents caused by AOp. Therefore, it should be properly controlled and forecasted to minimize the impacts on the environment and public health. In this paper, a Lasso and Elastic-Net Regularized Generalized Linear Model (GLMNET) was developed for predicting blast-induced AOp. The United States Bureau of Mines (USBM) empirical technique was also applied to estimate blast-induced AOp and compare with the developed GLMNET model. Nui Beo open-pit coal mine, Vietnam was selected as a case study. The performance indices are used to evaluate the performance of the models, including Root Mean Square Error (RMSE), Determination Coefficient (R2), and Mean Absolute Error (MAE). For this aim, 108 blasting events were investigated with the Maximum of explosive charge capacity, monitoring distance, powder factor, burden, and the length of stemming were considered as input variables for predicting AOp. As a result, a robust GLMNET model was found for predicting blast-induced AOp with an RMSE of 1.663, R2 of 0.975, and MAE of 1.413 on testing datasets. Whereas, the USBM empirical method only reached an RMSE of 2.982, R2 of 0.838, and MAE of 2.162 on testing datasets.

Słowa kluczowe

EN

Lasso model; open pit mines; explosives

PL

Lasso model; kopalnia odkrywkowa; wybuchy

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2019
• Tom: R. 21, nr 2/2
• Strony: 8--20
• Opis fizyczny: Bibliogr. 34 poz., tab., wykr., zdj.

Twórcy

autor

Bui Xuan‑Nam

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Nguyen Hoang

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Tran Quang Hieu

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Bui Hoang‑Bac

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, Hanoi, Vietnam

autor

Nguyen Quoc Long

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Nguyen Dinh An

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Le Thi Thu Hoa

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

autor

Pham Van Viet

• Hanoi University of Mining and Geology, Hanoi, Vietnam
• Center for Mining, Electro-Mechanical research, Hanoi, Vietnam

Bibliografia

• 1. Alel, M.N.A., et al. Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence. in Journal of Physics: Conference Series. 2018. IOP Publishing.
• 2. AminShokravi, A., et al., The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Engineering with Computers, 2018. 34(2): p. 277-285.
• 3. Amiri, M., et al., A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Engineering with Computers, 2016. 32(4): p. 631-644.
• 4. Armaghani, D.J., et al., Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arabian Journal of Geosciences, 2015. 8(12): p. 10937-10950.
• 5. Armaghani, D.J., M. Hasanipanah, and E.T. Mohamad, A combination of the ICAANN model to predict air-overpressure resulting from blasting. Engineering with Computers, 2016. 32(1): p. 155-171.
• 6. Armaghani, D.J., et al., Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications, 2018. 29(9): p. 619-629.
• 7. Bowen, I.G., E.R. Fletcher, and D.R. Richmond, Estimate of man's tolerance to the direct effects of air blast. 1968, LOVELACE FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH ALBUQUERQUE NM.
• 8. Cawley, G.C. Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs. in Neural Networks, 2006. IJCNN'06. International Joint Conference on. 2006. IEEE.
• 9. company, N.B., Summary report of production in 2010, Nui Beo (unpublish report). 2010.
• 10. Dismuke, C. and R. Lindrooth, Ordinary least squares. Methods and Designs for Outcomes Research, 2006. 93: p. 93-104.
• 11. Elsayed, N.M., N.V. Gorbunov, and V.E. Kagan, A proposed biochemical mechanism involving hemoglobin for blast overpressure-induced injury. Toxicology, 1997. 121(1): p. 81-90.
• 12. Faradonbeh, R.S., M. Monjezi, and D.J. Armaghani, Genetic programing and nonlinear multiple regression techniques to predict backbreak in blasting operation. Engineering with Computers, 2016. 32(1): p. 123-133.
• 13. Faradonbeh, R.S., et al., Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environmental monitoring and assessment, 2018. 190(6): p. 351.
• 14. Friedman, J., T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 2010. 33(1): p. 1.
• 15. Hajihassani, M., et al., Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics, 2014. 80: p. 57-67.
• 16. Hasanipanah, M., et al., Several non-linear models in estimating air-overpressure resulting from mine blasting. Engineering with Computers, 2016. 32(3): p. 441-455.
• 17. Hasanipanah, M., et al., Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Engineering with Computers, 2017. 33(1): p. 23-31.
• 18. Hastie, T. and J. Qian, Glmnet vignette. Retrieved June, 2014. 9(2016): p. 1-30.
• 19. Hoerl, A.E. and R.W. Kennard, Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 1970. 12(1): p. 55-67.
• 20. Khandelwal, M. and P. Kankar, Prediction of blast-induced air overpressure using support vector machine. Arabian Journal of Geosciences, 2011. 4(3-4): p. 427-433.
• 21. Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. in Ijcai. 1995. Montreal, Canada.
• 22. Kuzu, C., A. Fisne, and S. Ercelebi, Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Applied Acoustics, 2009. 70(3): p. 404-411.
• 23. Li, G., et al., The Empirical Relationship between Mining Industry Development and Environmental Pollution in China. International journal of environmental research and public health, 2017. 14(3): p. 254.
• 24. Loder, B. National Association of Australian State Road Authorities. in Australian Workshop for Senior ASEAN Transport Officials, 1985, Canberra. 1987.
• 25. Mahdiyar, A., A. Marto, and S.A. Mirhosseinei, Probabilistic air-overpressure simulation resulting from blasting operations. Environmental Earth Sciences, 2018. 77(4): p. 123.
• 26. Mayorga, M.A., The pathology of primary blast overpressure injury. Toxicology, 1997. 121(1): p. 17-28.
• 27. McKenzie, C., Quarry blast monitoring: technical and environmental perspectives. Quarry Management, 1990. 17: p. 23-4.
• 28. Mohamad, E.T., et al., Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environmental Earth Sciences, 2016. 75(2): p. 174.
• 29. Rezaei, M., M. Monjezi, and A.Y. Varjani, Development of a fuzzy model to predict flyrock in surface mining. Safety science, 2011. 49(2): p. 298-305.
• 30. Rosenthal, M.F. and G.L. Morlock, Blasting guidance manual. 1987.
• 31. Saadat, M., M. Khandelwal, and M. Monjezi, An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. Journal of Rock Mechanics and Geotechnical Engineering, 2014. 6(1): p. 67-76.
• 32. Siskind, D.E., et al., Structure response and damage produced by airblast from surface mining. 1980: Citeseer.
• 33. Tibshirani, R., Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), 1996: p. 267-288.
• 34. Venables, W.N. and B.D. Ripley, Tree-based methods, in Modern Applied Statistics with S. 2002, Springer. p. 251-269.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).