Abstract
In this study, a coupling of generalized linear modeling (GLMNET) and nonlinear neural network modeling with multilayer perceptrons (MLPNN), called GLMNETs–MLPNN modeling, was conducted for predicting air over-pressure (AOp) induced by blasting in open-pit mines. Accordingly, six GLMNET models were developed first. Then, their predictions were bootstrap aggregated as the new predictors, and an optimal MLPNN model was developed based on these new predictors. To prove the improvement of the proposed GLMNETs–MLPNN model, the conventional models, such as GLMNET, support vector machine, MLPNN, random forest, and empirical, were considered and developed based on the same dataset. The results of the proposed model then were compared with that of the conventional models in terms of accurate prediction and modeling. The findings revealed that the bootstrap aggregating of six generalized linear models (i.e., GLMNET models) by a nonlinear model (i.e., MLPNN) could enhance the accuracy in predicting AOp with a root-mean-squared error (RMSE) of 2.266, determination coefficient (R2) of 0.916, and mean squared error (MAE) of 1.718. In contrast, the other stand-alone models provided poorer performances with RMSE of 2.981–4.686, R2 of 0.597–0.860, and MAE of 3.156–1.990. Besides, the sensitivity analysis results indicated that burden, stemming, distance, spacing and maximum explosive charge per delay were the most important parameters in predicting AOp.
Similar content being viewed by others
References
Akande, J., Aladejare, A., & Lawal, A. (2014). Evaluation of the environmental impacts of blasting in okorusu fluorspar mine, namibia. International Journal of Engineering and Technology, 4(2), 101–108.
Alel, M. N. A., Upom, M. R. A., Abdullah, R. A., & Abidin, M. H. Z. (2018). Optimizing Blasting’s Air Overpressure Prediction Model using Swarm Intelligence. In Journal of Physics: Conference Series, (Vol. 995, pp. 012046, Vol. 1): IOP Publishing.
AminShokravi, A., Eskandar, H., Derakhsh, A. M., Rad, H. N., & Ghanadi, A. (2018). The potential application of particle swarm optimization algorithm for forecasting the air-overpressure induced by mine blasting. Engineering with Computers, 34(2), 277–285.
Armaghani, D. J., Hajihassani, M., Marto, A., Faradonbeh, R. S., & Mohamad, E. T. (2015). Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environmental Monitoring and Assessment, 187(11), 666.
Armaghani, D. J., Hasanipanah, M., Amnieh, H. B., & Mohamad, E. T. (2018). Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Computing and Applications, 29(9), 457–465.
Azimi, Y., Khoshrou, S. H., & Osanloo, M. (2019). Prediction of blast induced ground vibration (BIGV) of quarry mining using hybrid genetic algorithm optimized artificial neural network. Measurement, 147, 106874.
Bakhtavar, E., Abdollahisharif, J., & Ahmadi, M. (2017). Reduction of the undesirable bench-blasting consequences with emphasis on ground vibration using a developed multi-objective stochastic programming. International Journal of Mining, Reclamation and Environment, 31(5), 333–345.
Baur, W., & Strassen, V. (1983). The complexity of partial derivatives. Theoretical computer science, 22(3), 317–330.
Bellman, R. E., Kagiwada, H., & Kalaba, R. E. (1965). Wengert’s numerical method for partial derivatives, orbit determination and quasilinearization. Communications of the ACM, 8(4), 231–232.
Boland, N., Charkhgard, H., & Savelsbergh, M. (2019). Preprocessing and cut generation techniques for multi-objective binary programming. European Journal of Operational Research, 274(3), 858–875.
Box, G. E., & Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211–243.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Bui, X.-N., Choi, Y., Atrushkevich, V., Nguyen, H., Tran, Q.-H., Long, N. Q., et al. (2019a). Prediction of blast-induced ground vibration intensity in open-pit mines using unmanned aerial vehicle and a novel intelligence system. Natural Resources Research. https://doi.org/10.1007/s11053-019-09573-7.
Bui, X.-N., Jaroonpattanapong, P., Nguyen, H., Tran, Q.-H., & Long, N. Q. (2019b). A novel Hybrid Model for predicting Blast-induced Ground Vibration Based on k-nearest neighbors and particle Swarm optimization. Scientific Reports, 9(1), 1–14.
Bui, X.-N., Nguyen, H., Le, H. A., Bui, H. B., & Do, N. H. (2019c). Prediction of blast-induced air over-pressure in open-pit mine: Assessment of different artificial intelligence techniques. Natural Resources Research. https://doi.org/10.1007/s11053-019-09461-0.
Bui, X. N., Nguyen, H., Tran, Q. H., Bui, H. B., Nguyen, Q. L., Nguyen, D. A., et al. (2019). A Lasso and Elastic-Net Regularized Generalized Linear Model for Predicting Blast-Induced Air Over-pressure in Open-Pit Mines. Inżynieria Mineralna, 21.
Çaylak, Ç., & Kaftan, İ. (2014). Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm. Acta Geophysica, 62(6), 1310–1327.
Chen, W., Hasanipanah, M., Rad, H. N., Armaghani, D. J., & Tahir, M. (2019). A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Engineering with Computers, pp. 1–17.
Cortes, C., & Vapnik, V. (1995). Support vector machine. Machine Learning, 20(3), 273–297.
Davitt, A. L., & Simon, J. R. (1983). Non-electric delay blasting method. Google Patents.
Dehnavi, A., Aghdam, I. N., Pradhan, B., & Varzandeh, M. H. M. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. CATENA, 135, 122–148.
Ding, Z., Nguyen, H., Bui, X.-N., Zhou, J., & Moayedi, H. (2019). Computational intelligence model for estimating intensity of blast-induced ground vibration in a mine based on imperialist competitive and extreme gradient boosting algorithms. Natural Resources Research. https://doi.org/10.1007/s11053-019-09548-8.
Dou, J., Yunus, A. P., Merghadi, A., Shirzadi, A., Nguyen, H., Hussain, Y., et al. (2020). Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Science of the Total Environment, 720, 137320.
Ebtehaj, I., Bonakdari, H., Zeynoddin, M., Gharabaghi, B., & Azari, A. (2020). Evaluation of preprocessing techniques for improving the accuracy of stochastic rainfall forecast models. International Journal of Environmental Science and Technology, 17(1), 505–524.
Ewick, D. W., Sutula Jr, D. P., Welch, B. M., Sendek, A., & Eicke Jr, W. B. (1998). Explosive initiation system. Google Patents.
Fang, Q., Nguyen, H., Bui, X.-N., & Nguyen-Thoi, T. (2019). Prediction of blast-induced ground vibration in open-pit mines using a new technique based on imperialist competitive algorithm and M5rules. Natural Resources Research. https://doi.org/10.1007/s11053-019-09577-3.
Friedman, J., Hastie, T., & Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1), 1.
Guo, H., Nguyen, H., Bui, X.-N., & Armaghani, D. J. (2019). A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers. https://doi.org/10.1007/s00366-019-00833-x.
Guo, H., Nguyen, H., Bui, X.-N., & Armaghani, D. J. (2019a). A new technique to predict fly-rock in bench blasting based on an ensemble of support vector regression and GLMNET. Engineering with Computers, pp. 1–15.
Harandizadeh, H., & Armaghani, D. J. (2020). Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Applied Soft Computing, 106904.
Hasanipanah, M., Armaghani, D. J., Khamesi, H., Amnieh, H. B., & Ghoraba, S. (2016). Several non-linear models in estimating air-overpressure resulting from mine blasting. Engineering with Computers, 32(3), 441–455.
Hasanipanah, M., Shahnazar, A., Amnieh, H. B., & Armaghani, D. J. (2017). Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Engineering with Computers, 33(1), 23–31.
Hastie, T., & Qian, J. (2016). An Introduction to glmnet.
Huang, B., Liu, C., Fu, J., & Guan, H. (2011). Hydraulic fracturing after water pressure control blasting for increased fracturing. International Journal of Rock Mechanics and Mining Sciences, 48(6), 976–983.
Isa, I., Saad, Z., Omar, S., Osman, M., Ahmad, K., & Sakim, H. M. (2010). Suitable MLP network activation functions for breast cancer and thyroid disease detection. In 2010 Second International Conference on Computational Intelligence, Modelling and Simulation, (pp. 39–44): IEEE
Keshtegar, B., Hasanipanah, M., Bakhshayeshi, I., & Sarafraz, M. E. (2019). A novel nonlinear modeling for the prediction of blast-induced airblast using a modified conjugate FR method. Measurement, 131, 35–41.
Khandelwal, M., & Kankar, P. (2011). Prediction of blast-induced air overpressure using support vector machine. Arabian Journal of Geosciences, 4(3–4), 427–433.
Liu, W., Moayedi, H., Nguyen, H., Lyu, Z., & Bui, D. T. (2019). Proposing two new metaheuristic algorithms of ALO-MLP and SHO-MLP in predicting bearing capacity of circular footing located on horizontal multilayer soil. Engineering with Computers, pp. 1–11.
Moayedi, H., Foong, L. K., & Nguyen, H. (2020). Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42(11), 1–11.
Mohamad, E. T., Armaghani, D. J., Hasanipanah, M., Murlidhar, B. R., & Alel, M. N. A. (2016). Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique. Environmental Earth Sciences, 75(2), 174.
Ngo, T., Mendis, P., Gupta, A., & Ramsay, J. (2007). Blast loading and blast effects on structures–an overview. Electronic Journal of Structural Engineering, 7(S1), 76–91.
Ngo, T. P. T., Ngo, L. H., Nguyen, K. Q., Bui, T. T., Van Tran, P., Nhu, H. V., et al. (2020). Applying Random Forest approach in forecasting flash flood susceptibility area in Lao Cai region. Journal of Mining and Earth Sciences, 61(5), 30–42.
Nguyen, H. (2020). Application of the k-nearest neighbors algorithm for predicting blast-induced ground vibration in open-pit coal mines: a case study. Journal of Mining and Earth Sciences, 61(6), 22–29.
Nguyen, H., & Bui, X.-N. (2019). Predicting blast-induced air overpressure: a robust artificial intelligence system based on artificial neural networks and random forest. Natural Resources Research, 28(3), 893–907.
Nguyen, H., & Bui, X.-N. (2020a). Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach. Applied Soft Computing, 92, 106292.
Nguyen, H., & Bui, X.-N. (2020b). Soft computing models for predicting blast-induced air over-pressure: A novel artificial intelligence approach. Applied Soft Computing, p. 106292.
Nguyen, H., Bui, X.-N., Bui, H.-B., & Mai, N.-L. (2018). A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine Vietnam. Neural Computing and Applications, 32(8), 3939–3955.
Nguyen, H., Bui, X.-N., Bui, H.-B., & Cuong, D. T. (2019a). Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: A case study. Acta Geophysica, 67(2), 477–490.
Nguyen, H., Bui, X.-N., & Moayedi, H. (2019b). A comparison of advanced computational models and experimental techniques in predicting blast-induced ground vibration in open-pit coal mine. Acta Geophysica, 67(4), 1025–1037.
Nguyen, H., Bui, X.-N., Tran, Q.-H., & Mai, N.-L. (2019c). A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms. Applied Soft Computing, 77, 376–386.
Nguyen, H., Bui, X.-N., Tran, Q.-H., Van Hoa, P., Nguyen, D.-A., Hoa, L. T. T., et al. (2020a). A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophysica. https://doi.org/10.1007/s11600-019-00396-x.
Nguyen, H., Bui, N. X., Tran, H. Q., & Le, G. H. T. (2020b). A novel soft computing model for predicting blast - induced ground vibration in open - pit mines using gene expression programming. Journal of Mining and Earth Sciences, 61(5), 107–116.
Nguyen, A. D., Van Nhu, B., Tran, B. D., Van Pham, H., & Nguyen, T. A. (2020c). Definition of amount explosive per blast for spillway at the Nui Mot lake - Binh Dinh province. Journal of Mining and Earth Sciences, 61(5), 117–124.
Ojha, V. K., Abraham, A., & Snášel, V. (2017). Metaheuristic design of feedforward neural networks: A review of two decades of research. Engineering Applications of Artificial Intelligence, 60, 97–116.
Ozer, U., Karadogan, A., Ozyurt, M. C., Sertabipoglu, Z., & Sahinoglu, U. K. (2020). Modelling of blasting-induced air overpressure wave propagation under atmospheric conditions by using ANN model. Arabian Journal of Geosciences, 13(16), 1–11.
Parente, A., & Sutherland, J. C. (2013). Principal component analysis of turbulent combustion data: Data pre-processing and manifold sensitivity. Combustion and Flame, 160(2), 340–350.
Pavlidis, D. E., Mallouchos, A., Ercolini, D., Panagou, E. Z., & Nychas, G.-J.E. (2019). A volatilomics approach for off-line discrimination of minced beef and pork meat and their admixture using HS-SPME GC/MS in tandem with multivariate data analysis. Meat Science, 151, 43–53.
Remennikov, A., & Carolan, D. (2006). Blast effects and vulnerability of building structures from terrorist attack. Australian Journal of Structural Engineering, 7(1), 1–11.
Rokach, L. (2016). Decision forest: Twenty years of research. Information Fusion, 27, 111–125.
Rosenfeld, J. V., McFarlane, A. C., Bragge, P., Armonda, R. A., Grimes, J. B., & Ling, G. S. (2013). Blast-related traumatic brain injury. The Lancet Neurology, 12(9), 882–893.
Salarian, A., Russmann, H., Vingerhoets, F. J., Burkhard, P. R., & Aminian, K. (2007). Ambulatory monitoring of physical activities in patients with Parkinson’s disease. IEEE Transactions on Biomedical Engineering, 54(12), 2296–2299.
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 43–48.
Tran, B. D., Vu T. D., Van Pham, V., Nguyen, T. A., Nguyen, A. D., & Le, G. H. T. (2020). Developing a mathematical model to optimize long-term quarrying planing for limestone quarries producing cement in Vietnam. Journal of Mining and Earth Sciences, 61(5), 58–70.
Turgut, Z., Üstebay, S., Aydın, G. Z. G., & Sertbaş, A. Deep learning in indoor localization using WiFi. In International Telecommunications Conference, 2019 (pp. 101–110): Springer.
Yu, Z., Shi, X., Zhou, J., Gou, Y., Huo, X., Zhang, J., et al. (2020). A new multikernel relevance vector machine based on the HPSOGWO algorithm for predicting and controlling blast-induced ground vibration. Engineering with Computers, pp. 1–16.
Zhang, X., Nguyen, H., Bui, X.-N., Le Anh, H., Nguyen-Thoi, T., Moayedi, H., et al. (2020). Evaluating and predicting the stability of roadways in tunnelling and underground space using artificial neural network-based particle swarm optimization. Tunnelling and Underground Space Technology, 103, 103517.
Zhou, J., Qiu, Y., Zhu, S., Armaghani, D. J., Li, C., Nguyen, H., et al. (2021). Optimization of support vector machine through the use of metaheuristic algorithms in forecasting TBM advance rate. Engineering Applications of Artificial Intelligence, 97, 104015.
Acknowledgments
The authors would like to thank the Center for Mining, Electro-Mechanical research of Hanoi University of Mining and Geology (HUMG), Hanoi, Vietnam, and the research team of Innovations for Sustainable and Responsible Mining (ISRM) of HUMG.
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Nguyen, H., Bui, XN. & Tran, QH. Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs–MLPNN). Nat Resour Res 30, 2629–2646 (2021). https://doi.org/10.1007/s11053-021-09822-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11053-021-09822-8