Skip to main content

Advertisement

SpringerLink
Log in

A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine

  • Research Article - Solid Earth Sciences
  • Published:
Acta Geophysica Aims and scope Submit manuscript

A Correction to this article was published on 30 March 2021

This article has been updated

Abstract

This study aims to take into account the feasibility of three ensemble machine learning algorithms for predicting blast-induced air over-pressure (AOp) in open-pit mine, including gradient boosting machine (GBM), random forest (RF), and Cubist. An empirical technique was also applied to predict AOp and compared with those of the ensemble models. To employ this study, 146 events of blast were investigated with 80% of the total database (approximately 118 blasting events) being used for developing the models, whereas the rest (20% ~ 28 blasts) were used to validate the models’ accuracy. RMSE, MAE, and R2 were used as performance indices for evaluating the reliability of the models. The findings revealed that the ensemble models yielded more precise accuracy than those of the empirical model. Of the ensemble models, the Cubist model provided better performance than those of RF and GBM models with RMSE, MAE, and R2 of 2.483, 0.976, and 0.956, respectively, whereas the RF and GBM models provided poorer accuracy with an RMSE of 2.579, 2.721; R2 of 0.953, 0.950, and MAE of 1.103, 1.498, respectively. In contrast, the empirical model was interpreted as the poorest model with an RMSE of 4.448, R2 of 0.872, and MAE of 3.719. In addition, other findings indicated that explosive charge capacity, spacing, stemming, monitoring distance, and air humidity were the most important inputs for the AOp predictive models using artificial intelligence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Change history

References

  • Alel MNA, Upom MRA, Abdullah RA, Abidin MHZ (2018) Optimizing blasting’s air overpressure prediction model using swarm intelligence. In: Journal of Physics: Conference Series, vol 1. IOP Publishing, p 012046

  • AminShokravi A, Eskandar H, Derakhsh AM, Rad HN, Ghanadi A (2018) The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Eng Comput 34(2):277–285. https://doi.org/10.1007/s00366-017-0539-5

    Article  Google Scholar 

  • Amiri M, Amnieh HB, Hasanipanah M, Khanli LM (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput 32(4):631–644

    Article  Google Scholar 

  • Armaghani DJ, Hajihassani M, Marto A, Faradonbeh RS, Mohamad ET (2015) Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environ Monit Assess 187(11):666

    Article  Google Scholar 

  • Armaghani DJ, Hasanipanah M, Mohamad ET (2016) A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Eng Comput 32(1):155–171

    Article  Google Scholar 

  • Asteris PG, Nikoo M (2019) Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Comput Appl. https://doi.org/10.1007/s00521-018-03965-1

    Article  Google Scholar 

  • Asteris P, Kolovos K, Douvika M, Roinos K (2016) Prediction of self-compacting concrete strength using artificial neural networks. Eur J Environ Civ Eng 20(sup1):s102–s122

    Article  Google Scholar 

  • Bach NV, Nam BX, An ND, Hung TK (2012) Determination of blast-induced ground vibration for non-electric delay blasting (in Vietnamse). J SciTechnol Hanoi Univ Min Geol 38(02):25–28

    Google Scholar 

  • Bernat K, Drzewiecki W (2015) A study of selected textural features usefulness for impervious surface coverage estimation using Landsat images. In: Image and signal processing for remote sensing XXI, 2015. International society for optics and photonics, p 964327

  • Breiman L (2001) Random for Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Bui XN, Nguyen H, Le HA, Bui HB, Do NH (2019a) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Resour Res. https://doi.org/10.1007/s11053-019-09461-0

    Article  Google Scholar 

  • Bui X-N, Nguyen H, Le H-A, Bui H-B, Do N-H (2019b) Prediction of blast-induced air over-pressure in open-pit mine: assessment of different artificial intelligence techniques. Nat Res Res. https://doi.org/10.1007/s11053-019-09461-0

    Article  Google Scholar 

  • Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, Chen C-W, Khosravi K, Yang Y, Pham BT (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346

    Article  Google Scholar 

  • Drzewiecki W (2016) Comparison of selected machine learning algorithms for sub-pixel imperviousness change assessment. In: Geodetic Congress (Geomatics), Baltic, 2016. IEEE, pp 67–72

  • Friedman J (1999) Greedy function approximation: A stochastic boosting machine. Department of Statistics Stanford University

  • Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189–1232

    Article  Google Scholar 

  • Friedman JH (2002) Stochastic gradient boosting. Comput Stat Data Anal 38(4):367–378

    Article  Google Scholar 

  • Gao W, Guirao JL, Basavanagoud B, Wu J (2018) Partial multi-dividing ontology learning algorithm. Inf Sci 467:35–58

    Article  Google Scholar 

  • Gao W, Alqahtani AS, Mubarakali A, Mavaluru D, Khalafi S (2019) Developing an innovative soft computing scheme for prediction of air overpressure resulting from mine blasting using GMDH optimized by GA. Eng Comput. https://doi.org/10.1007/s00366-019-00720-5

    Article  Google Scholar 

  • Hajihassani M, Armaghani DJ, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  • Hasanipanah M, Armaghani DJ, Khamesi H, Amnieh HB, Ghoraba S (2016) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput 32(3):441–455

    Article  Google Scholar 

  • Hustrulid W (1999) Blasting principles for open-pit blasting: theoretical foundations. Balkema, Rotterdam

    Google Scholar 

  • Jhanwar J, Cakraborty A, Anireddy H, Jethwa J (1999) Application of air decks in production blasting to improve fragmentation and economics of an open pit mine. Geotech Geol Eng 17(1):37–57

    Article  Google Scholar 

  • Khandelwal M, Kankar P (2011) Prediction of blast-induced air overpressure using support vector machine. Arab J Geosci 4(3–4):427–433

    Article  Google Scholar 

  • Khandelwal M, Singh T (2005) Prediction of blast induced air overpressure in opencast mine. Noise Vib Worldw 36(2):7–16

    Article  Google Scholar 

  • Kuhn M, Weston S, Keefer C, Coulter N (2012) Cubist models for regression. R package Vignette R package version 00 18

  • Kuhn M, Weston S, Keefer C, Kuhn MM (2018) Package ‘Cubist’

  • Kuzu C, Fisne A, Ercelebi S (2009) Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries. Appl Acoust 70(3):404–411

    Article  Google Scholar 

  • Loder B (1985) National Association of Australian State Road Authorities. In: Australian Workshop for Senior ASEAN Transport Officials, 1985, Canberra, 1987

  • Mahdiyar A, Marto A, Mirhosseinei SA (2018) Probabilistic air-overpressure simulation resulting from blasting operations. Environ Earth Sci 77(4):123

    Article  Google Scholar 

  • McKenzie C (1990) Quarry blast monitoring: technical and environmental perspectives. Quarry Manag 17:23–24

    Google Scholar 

  • Mohamad ET, Armaghani DJ, Hasanipanah M, Murlidhar BR, Alel MNA (2016) Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environ Earth Sci 75(2):174

    Article  Google Scholar 

  • Montahaei M, Oskooi B (2014) Magnetotelluric inversion for azimuthally anisotropic resistivities employing artificial neural networks. Acta Geophys 62(1):12–43. https://doi.org/10.2478/s11600-013-0164-7

    Article  Google Scholar 

  • Naganna SR, Deka PC (2019) Artificial intelligence approaches for spatial modeling of streambed hydraulic conductivity. Acta Geophys. https://doi.org/10.1007/s11600-019-00283-5

    Article  Google Scholar 

  • Nguyen H, Bui X-N (2018a) Feasibility of artificial neural network in predicting blast-induced air overpressure in open-pit mine. J Min Ind 01:60–66

    Google Scholar 

  • Nguyen H, Bui X-N (2018b) Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Nat Resour Res. https://doi.org/10.1007/s11053-018-9424-1

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Tran Q-H (2017) Prediction of blast-induced air overpressure in Deo Nai open-pit coal mine using Random Forest algorithm. J Min Ind 06:47–53

    Google Scholar 

  • Nguyen H, Bui X-N, Tran Q-H, Le T-Q, Do N-H, Hoa LTT (2018a) Evaluating and predicting blast-induced ground vibration in open-cast mine using ANN: a case study in Vietnam. SN Appl Sci 1(1):125. https://doi.org/10.1007/s42452-018-0136-2

    Article  Google Scholar 

  • Nguyen H, Bui XN, Bui HB, Mai NL (2018b) A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3717-5

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019a) Predicting blast-induced peak particle velocity using BGAMs, ANN and SVM: a case study at the Nui Beo open-pit coal mine in Vietnam. Environ Earth Sci 78(15):479. https://doi.org/10.1007/s12665-019-8491-x

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Tran Q-H, Mai N-L (2019b) A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Appl Soft Comput 77:376–386. https://doi.org/10.1016/j.asoc.2019.01.042

    Article  Google Scholar 

  • Nguyen H, Moayedi H, Foong LK, Al Najjar HAH, Jusoh WAW, Rashid ASA, Jamali J (2019c) Optimizing ANN models with PSO for predicting short building seismic response. Eng Comput. https://doi.org/10.1007/s00366-019-00733-0

    Article  Google Scholar 

  • Nguyen H, Drebenstedt C, Bui X-N, Bui DT (2019d) Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09470-z

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Bui H-B, Cuong DT (2019e) Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophys 67(2):477–490. https://doi.org/10.1007/s11600-019-00268-4

    Article  Google Scholar 

  • Nguyen H, Bui X-N, Moayedi H (2019f) A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophys. https://doi.org/10.1007/s11600-019-00304-3

    Article  Google Scholar 

  • Piasecki A, Jurasz J, Adamowski JF (2018) Forecasting surface water-level fluctuations of a small glacial lake in Poland using a wavelet-based artificial intelligence method. Acta Geophys 66(5):1093–1107. https://doi.org/10.1007/s11600-018-0183-5

    Article  Google Scholar 

  • Pierini JO, Lovallo M, Telesca L, Gómez EA (2013) Investigating prediction performance of an artificial neural network and a numerical model of the tidal signal at Puerto Belgrano, Bahia Blanca Estuary (Argentina). Acta Geophys 61(6):1522–1537. https://doi.org/10.2478/s11600-012-0093-x

    Article  Google Scholar 

  • Quinlan JR (1992) Learning with continuous classes. In: 5th Australian joint conference on artificial intelligence. Singapore, pp 343–348

  • Quinlan R (2004) Data mining tools See5 and C5. 0

  • Rahmani Y, Farnood Ahmadi F (2018) Application of InSAR in measuring Earth’s surface deformation caused by groundwater extraction and modeling its behavior using time series analysis by artificial neural networks. Acta Geophys 66(5):1171–1184. https://doi.org/10.1007/s11600-018-0182-6

    Article  Google Scholar 

  • Rulequest (2016a) Data Mining with Cubist. https://www.rulequest.com/cubist-win.html. Accessed 26 Feb 2019

  • Rulequest (2016b) Data Mining with Cubist. https://www.rulequestcom/cubist-info.html RuleQuest Research Pty Ltd.,St. Ives, NSW, Australia

  • Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010) Variance based sensitivity analysis of model output Design and estimator for the total sensitivity index. Comput Phys Commun 181(2):259–270

    Article  Google Scholar 

  • Shang Y, Nguyen H, Bui X-N, Tran Q-H, Moayedi H (2019) A novel artificial intelligence approach to predict blast-induced ground vibration in open-pit mines based on the firefly algorithm and artificial neural network. Nat Resour Res. https://doi.org/10.1007/s11053-019-09503-7

    Article  Google Scholar 

  • Siskind DE, Stachura VJ, Stagg MS, Kopp JW (1980) Structure response and damage produced by airblast from surface mining. Citeseer

  • Tarantola S, Gatelli D, Kucherenko S, Mauntz W (2007) Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliab Eng Syst Saf 92(7):957–960

    Article  Google Scholar 

  • Vigneau E, Courcoux P, Symoneaux R, Guérin L, Villière A (2018) Random forests: a machine learning methodology to highlight the volatile organic compounds involved in olfactory perception. Food Qual Prefer 68:135–145

    Article  Google Scholar 

  • Vinacomin (2015) Report on geological exploration of Coc Sau open pit coal mine, Quang Ninh, Vietnam (in Vietnamse-unpublished). VINACOMIN, Vietnam

  • Walter E (1990) Surface blast design. Prentice Hall, New Jersey

    Google Scholar 

  • Wiszniowski J (2016) Applying the general regression neural network to ground motion prediction equations of induced events in the Legnica-Głogów copper district in Poland. Acta Geophys 64(6):2430–2448. https://doi.org/10.1515/acgeo-2016-0104

    Article  Google Scholar 

  • Zhang X, Nguyen H, Bui X-N, Tran Q-H, Nguyen D-A, Bui DT, Moayedi H (2019) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Nat Resour Res. https://doi.org/10.1007/s11053-019-09492-7

    Article  Google Scholar 

  • Zhou J, Li E, Yang S, Wang M, Shi X, Yao S, Mitri HS (2019a) Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories. Saf Sci 118:505–518

    Article  Google Scholar 

  • Zhou J, Nekouie A, Arslan CA, Pham BT, Hasanipanah M (2019b) Novel approach for forecasting the blast-induced AOp using a hybrid fuzzy system and firefly algorithm. Eng Comput. https://doi.org/10.1007/s00366-019-00725-0

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam; Duy Tan University, Da Nang, Vietnam; and the Center for Mining, Electro-Mechanical research of HUMG.

Author information

Authors and Affiliations

  1. Department of Surface Mining, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran, Pham Van Hoa, Dinh-An Nguyen, Le Thi Thu Hoa, Qui-Thao Le, Ngoc-Hoan Do & Tran Dinh Bao

  2. Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang Ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang Nguyen, Xuan-Nam Bui & Qui-Thao Le

  3. Faculty of Mining, Saint-Petersburg Mining University, Saint Petersburg, Russia

    Ngoc-Hoan Do

  4. Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang-Bac Bui

  5. Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien St., Duc Thang ward, Bac Tu Liem Dist., Hanoi, Vietnam

    Hoang-Bac Bui

  6. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  7. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

Authors
  1. Hoang Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuan-Nam Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quang-Hieu Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pham Van Hoa
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dinh-An Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Le Thi Thu Hoa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qui-Thao Le
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ngoc-Hoan Do
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Tran Dinh Bao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hoang-Bac Bui
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hossein Moayedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuan-Nam Bui.

Ethics declarations

Conflict of interest

Authors declare that they have no conflict of interest

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, H., Bui, XN., Tran, QH. et al. A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophys. 68, 325–336 (2020). https://doi.org/10.1007/s11600-019-00396-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11600-019-00396-x

Keywords

Search

Navigation