Prediction of Vibration Velocity Generated in Mine Blasting Using Support Vector Regression Improved by Optimization Algorithms

  • Haiqing Yang
  • Hima Nikafshan Rad
  • Mahdi HasanipanahEmail author
  • Hassan Bakhshandeh Amnieh
  • Atefeh Nekouie
Original Paper


Ground vibration generated from blasting is a detrimental side effect of the use of explosives to break the rock mass in mines. Therefore, accurately predicting ground vibration is a practical need, especially for safety issues. This research proposes hybrid artificial intelligence schemes for predicting ground vibration. The approaches are based on support vector regression (SVR) optimized with firefly algorithm (FFA), genetic algorithm (GA), and particle swarm optimization (PSO). Additionally, a hybrid FFA and artificial neural network (ANN) model and several well-known empirical models were also employed in this study. In the predictive modeling process, 90 sets of data, collected from two quarry mines in Iran, divided into two datasets, namely a training dataset and a testing dataset, were used. After model development, to provide an objective assessment of the predictive model performances, their results were compared based on several well-known and popular statistical criteria. FFA-SVR exhibits much more efficiency and reliability than PSO-SVR, GA-SVR, FFA–ANN models in terms of ground vibration prediction, indicating the superiority of FFA over PSO and GA in the SVR training.


Blasting Ground vibration Support vector regression Optimization algorithms 

Supplementary material

11053_2019_9597_MOESM1_ESM.pdf (157 kb)
Supplementary material 1 (PDF 156 kb)


  1. Abdi, M. J., & Giveki, D. (2013). Automatic detection of erythemato-squamous diseases using PSO–SVM based on association rules. Engineering Applications of Artificial Intelligence,26, 603–608.CrossRefGoogle Scholar
  2. Akande, K. O., Owolabi, T. O., Olatunji, S. O., & AbdulRaheem, A. (2017). A hybrid particle swarm optimization and support vector regression model for modelling permeability prediction of hydrocarbon reservoir. Journal of Petroleum Science and Engineering,150, 43–53.CrossRefGoogle Scholar
  3. Ambraseys, N. R., & Hendron, A. J. (1968). Dynamic behavior of rock masses: rock mechanics in engineering practices. London: Wiley.Google Scholar
  4. Amini, H., Gholami, R., Monjezi, M., Torabi, S. R., & Zadhesh, J. (2012). Evaluation of flyrock phenomenon due to blasting operation by support vector machine. Neural Computing and Applications,21, 2077–2085.CrossRefGoogle Scholar
  5. Apostolopoulos, T., & Vlachos, A. (2010). Application of the firefly algorithm for solving the economic emissions load dispatch problem. International Journal of Combinatorics,2011, 523806. Scholar
  6. Arthur, C. K., Temeng, V. A., & Ziggah, Y. Y. (2019). Novel approach to predicting blast-induced ground vibration using Gaussian process regression. Engineering with Computers. Scholar
  7. Asteris, P. G., Apostolopoulou, M., Skentou, A. D., & Antonia Moropoulou, A. (2019). Application of artificial neural networks for the prediction of the compressive strength of cement-based mortars. Computers and Concrete,24(4), 329–345.Google Scholar
  8. Asteris, P. G., & Nikoo, M. (2019). Artificial bee colony-based neural network for the prediction of the fundamental period of infilled frame structures. Neural Computing and Applications. Scholar
  9. Asteris, P. G., Roussis, P. C., & Douvika, M. G. (2017). Feed-forward neural network prediction of the mechanical properties of sandcrete materials. Sensors,17(6), 1344.CrossRefGoogle Scholar
  10. Asteris, P. G., Tsaris, A. K., Cavaleri, L., Repapis, C. C., Papalou, A., Di Trapani, F., et al. (2016). Prediction of the fundamental period of infilled RC frame structures using artificial neural networks. Computational Intelligence and Neuroscience,2016, 5104907. Scholar
  11. Behzadafshar, K., Mohebbi, F., Soltani Tehrani, M., Hasanipanah, M., & Tabrizi, M. (2018). Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm. Engineering Computation,35(4), 1774–1787.CrossRefGoogle Scholar
  12. Cavaleri, L., Asteris, P. G., Psyllaki, P. P., Douvika, M. G., Skentou, A. D., & Vaxevanidis, N. M. (2019). Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks. Applied Sciences,9(14), 2788.CrossRefGoogle Scholar
  13. Cavaleri, L., Chatzarakis, G. E., Di Trapani, F., Douvika, M. G., Roinos, K., Vaxevanidis, N. M., et al. (2017). Modeling of surface roughness in electro-discharge machining using artificial neural networks. Advances in Materials Research,6(2), 169–184.Google Scholar
  14. Dang, N. M., Anh, D. T., & Dang, T. D. (2019). ANN optimized by PSO and Firefly algorithms for predicting scour depths around bridge piers. Engineering with Computers. Scholar
  15. Davis, L. (1991). Handbook of genetic algorithms. New York: Van Nostrand Reinhold.Google Scholar
  16. Duvall, W. I., & Petkof, B. (1959). Spherical propagation of explosion generated strain pulses in rock. US Bureau of Mines Report of Investigation,5483, 1959.Google Scholar
  17. Elbisy, M. S. (2015). Support vector machine and regression analysis to predict the field hydraulic conductivity of sandy soil. KSCE Journal of Civil Engineering,19(7), 2307–2316.CrossRefGoogle Scholar
  18. Elevli, B., & Arpaz, E. (2010). Evaluation of parameters affected on the blast induced ground vibration (BIGV) by using relation diagram method (RDM). Acta Montanistica Slovaca,15(4), 261–268.Google Scholar
  19. Fernandes Junior, F. E., & Yen, G. G. (2019). Particle swarm optimization of deep neural networks architectures for image classification. Swarm and Evolutionary Computation BASE DATA. Scholar
  20. Fister, I., Yang, X. S., & Brest, J. (2013). A comprehensive review of firefly algorithms. Swarm and Evolutionary Computation,13, 34–46.CrossRefGoogle Scholar
  21. Gandomi, A. H., Yang, X. S., & Alavi, A. H. (2011). Mixed variable structural optimization using firefly algorithm. Computers & Structures,89(23), 2325–2336.CrossRefGoogle Scholar
  22. Gao, W., Alqahtani, A. S., 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. Engineering with Computers,35(131), 1–8.Google Scholar
  23. Ghaedi, M., Dashtian, K., Ghaedib, A. M., & Dehghanianc, N. (2016). A hybrid model of support vector regression with genetic algorithm for forecasting adsorption of malachite green onto multi-walled carbon nanotubes: central composite design optimization. Physical Chemistry Chemical Physics,18, 13310–13321.CrossRefGoogle Scholar
  24. Ghasemi, E., Ataei, M., & Hashemolhosseini, H. (2013). Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining. Journal of Vibration and Control,19(5), 755–770.CrossRefGoogle Scholar
  25. Goldberg, D. E. (1989). Genetic algorithms in search, optimization and machine learning. Boston: Addison-Wesley.Google Scholar
  26. Hajihassani, M., Jahed Armaghani, D., & Kalatehjari, R. (2018). Applications of particle swarm optimization in geotechnical engineering: a comprehensive review. Geotechnical and Geological Engineering,36(2), 705–722.CrossRefGoogle Scholar
  27. Hajihassani, M., Jahed Armaghani, D., Marto, A., & Mohamad, E. T. (2015a). Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment,74(3), 873–886.CrossRefGoogle Scholar
  28. Hajihassani, M., Jahed Armaghani, D., Monjezi, M., Mohamad, E. T., & Marto, A. (2015b). Blast-induced air and ground vibration prediction: A particle swarm optimization-based artificial neural network approach. Environmental Earth Sciences,74, 2799–2817.CrossRefGoogle Scholar
  29. Hajihassani, M., Jahed Armaghani, D., Sohaei, H., Mohamad, E. T., & Marto, A. (2014). Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Applied Acoustics,80, 57–67.CrossRefGoogle Scholar
  30. Hasanipanah, M., Amnieh, H. B., Arab, H., & Zamzam, M. S. (2018a). Feasibility of PSO–ANFIS model to estimate rock fragmentation produced by mine blasting. Neural Computing and Applications,30(4), 1015–1024.CrossRefGoogle Scholar
  31. Hasanipanah, M., Amnieh, H. B., Khamesi, H., Armaghani, D. J., Golzar, S. B., & Shahnazar, A. (2018b). Prediction of an environmental issue of mine blasting: An imperialistic competitive algorithm-based fuzzy system. International Journal of Environmental Science and Technology,15(3), 551–560.CrossRefGoogle Scholar
  32. Hasanipanah, M., Armaghani, D. J., Amnieh, H. B., Majid, M. Z. A., & Tahir, M. M. D. (2017a). Application of PSO to develop a powerful equation for prediction of flyrock due to blasting. Neural Computing and Applications,28(1), 1043–1050.CrossRefGoogle Scholar
  33. Hasanipanah, M., Faradonbeh, R. S., Amnieh, H. B., Armaghani, D. J., & Monjezi, M. (2017b). Forecasting blast-induced ground vibration developing a CART model. Engineering with Computers,33(2), 307–316.CrossRefGoogle Scholar
  34. Hasanipanah, M., Faradonbeh, R. S., Armaghani, D. J., Amnieh, H. B., & Khandelwal, M. (2017c). Development of a precise model for prediction of blast-induced flyrock using regression tree technique. Environmental Earth Sciences,76(1), 27.CrossRefGoogle Scholar
  35. Hasanipanah, M., Golzar, S. B., Larki, I. A., Maryaki, M. Y., & Ghahremanians, T. (2017d). Estimation of blast-induced ground vibration through a soft computing framework. Engineering with Computers,33(4), 951–959.CrossRefGoogle Scholar
  36. Hasanipanah, M., Monjezi, M., Shahnazar, A., Jahed Armaghani, D., & Farazmand, A. (2015). Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement,75, 289–297.CrossRefGoogle Scholar
  37. Hasanipanah, M., Naderi, R., Kashir, J., Noorani, S. A., & Zeynali Aaq Qaleh, A. (2017e). Prediction of blast produced ground vibration using particle swarm optimization. Engineering with Computers,33(2), 173–179.CrossRefGoogle Scholar
  38. Hasanipanah, M., Noorian-Bidgoli, M., Armaghani, D. J., & Khamesi, H. (2016). Feasibility of PSO–ANN model for predicting surface settlement caused by tunneling. Engineering with Computers,32(4), 705–715.CrossRefGoogle Scholar
  39. Hasanipanah, M., Shahnazar, A., Amnieh, H. B., & Armaghani, D. J. (2017f). Prediction of air-overpressure caused by mine blasting using a new hybrid PSO–SVR model. Engineering with Computers,33(1), 23–31.CrossRefGoogle Scholar
  40. Hasanipanah, M., Shahnazar, A., Arab, H., Golzar, S. B., & Amiri, M. (2017g). Developing a new hybrid-AI model to predict blast-induced backbreak. Engineering with Computers,33(3), 349–359.CrossRefGoogle Scholar
  41. Hecht-Nielsen, R. (1987). Kolmogorov’s mapping neural network existence theorem. In Proceedings of the first IEEE international conference on neural networks (pp. 11–14). San Diego, CA, USA.Google Scholar
  42. Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor: The University of Michigan Press.Google Scholar
  43. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural Networks,2, 359–366.CrossRefGoogle Scholar
  44. Indian Standard Institute. (1973). Criteria for safety and design of structures subjected to underground blast. ISI Bull IS-6922.Google Scholar
  45. Jahed Armaghani, D., Hajihassani, M., Mohamad, E. T., Marto, A., & Noorani, S. A. (2014). Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences,7, 5383–5396.CrossRefGoogle Scholar
  46. Jahed Armaghani, D., Hasanipanah, M., Bakhshandeh Amnieh, H., & Mohamad, E. T. (2018). Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Computing and Applications,29(9), 457–465.CrossRefGoogle Scholar
  47. Jahed Armaghani, D., Hasanipanah, M., Bakhshandeh Amnieh, H., Tien Bui, D., Mehrabi, P., & Khorami, M. (2019). Development of a novel hybrid intelligent model for solving engineering problems using GS-GMDH algorithm. Engineering with Computers. Scholar
  48. Jahed Armaghani, D., Raja, S. N. S. B., Faizi, K., & Rashid, A. S. A. (2015). Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Computing and Applications,28(2), 391–405.CrossRefGoogle Scholar
  49. Jiang, W., Arslan, C. A., Soltani Tehrani, M., Khorami, M., & Hasanipanah, M. (2019). Simulating the peak particle velocity in rock blasting projects using a neuro-fuzzy inference system. Engineering with Computers,35(4), 1203–1211.CrossRefGoogle Scholar
  50. Kennedy, J., & Eberhart, R. C. (2001). Swarm intelligence. San Diego, CA: Academic Press.Google Scholar
  51. 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.CrossRefGoogle Scholar
  52. Khandelwal, M. (2010). Evaluation and prediction of blast induced ground vibration using support vector machine. International Journal of Rock Mechanics and Mining Sciences,47(3), 509–516.CrossRefGoogle Scholar
  53. Khandelwal, M. (2011). Blast-induced ground vibration prediction using support vector machine. Engineering with Computers,27, 193–200.CrossRefGoogle Scholar
  54. Khandelwal, M., & Singh, T. N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine—A neural network approach. Journal Sound Vibration,289, 711–725.CrossRefGoogle Scholar
  55. Khandelwal, M., & Singh, T. N. (2009). Prediction of blast induced ground vibration using artificial neural network. International Journal of Rock Mechanics and Mining Sciences,46, 1214–1222.CrossRefGoogle Scholar
  56. Li, Y., Zhang, S., & Zeng, X. (2009). Research of multi-population agent genetic algorithm for feature selection. Expert Systems with Applications,36(3), 11570–11581.CrossRefGoogle Scholar
  57. Lu, X., Hasanipanah, M., Brindhadevi, K., Amnieh, H. B., & Khalafi, S. (2019). ORELM: A novel machine learning approach for prediction of flyrock in mine blasting. Natural Resources Research. Scholar
  58. Luo, Z., Hasanipanah, M., Amnieh, H. B., Brindhadevi, K., & Tahir, M. M. (2019). GA-SVR: A novel hybrid data-driven model to simulate vertical load capacity of driven piles. Engineering with Computers. Scholar
  59. Majumder, A., Das, A., & Das, P. K. (2016). A standard deviation based firefly algorithm for multi-objective optimization of WEDM process during machining of Indian RAFM steel. Neural Computing and Applications. Scholar
  60. Mirhosseyni, S. H. L., & Webb, P. (2009). A hybrid fuzzy knowledge-based expert system and genetic algorithm for efficient selection and assignment of material handling equipment. Expert Systems with Applications,36(3), 11875–11887.CrossRefGoogle Scholar
  61. Mojtahedi, S. F. F., Ebtehaj, I., Hasanipanah, M., Bonakdari, H., & Amnieh, H. B. (2019). Proposing a novel hybrid intelligent model for the simulation of particle size distribution resulting from blasting. Engineering with Computers,35(1), 47–56.CrossRefGoogle Scholar
  62. Monjezi, M., Hasanipanah, M., & Khandelwal, M. (2013). Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Computing and Applications,22(7–8), 1637–1643.CrossRefGoogle Scholar
  63. Murillo-Escobar, J., Sepulveda-Suescun, J. P., Correa, M. A., & Orrego-Metaute, D. (2019). Forecasting concentrations of air pollutants using support vector regression improved with particle swarm optimization: Case study in Aburrá Valley, Colombia. Urban Climate,29, 100473.CrossRefGoogle Scholar
  64. Nguyen, H., & Bui, X. N. (2018). 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.CrossRefGoogle Scholar
  65. 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.CrossRefGoogle Scholar
  66. Nguyen, H., Drebenstedt, C., Bui, X. N., & Bui, D. T. (2019b). Prediction of blast-induced ground vibration in an open-pit mine by a novel hybrid model based on clustering and artificial neural network. Natural Resources Research. Scholar
  67. Nikafshan Rad, H., Bakhshayeshi, I., Wan Jusoh, W. A., Tahir, M. M., & Kok Foong, L. (2019). Prediction of flyrock in mine blasting: A new computational intelligence approach. Natural Resources Research. Scholar
  68. Nikafshan Rad, H., Hasanipanah, M., Rezaei, M., & Eghlim, A. L. (2018). Developing a least squares support vector machine for estimating the blast-induced flyrock. Engineering with Computers,34(4), 709–717.CrossRefGoogle Scholar
  69. Olanrewaju Alade, I., Abd Rahman, M. A., & Saleh, T. A. (2019). Modeling and prediction of the specific heat capacity of Al2O3/water nanofluids using hybrid genetic algorithm/support vector regression model. Nano-Structures & Nano-Objects,17, 103–111.CrossRefGoogle Scholar
  70. Radojica, L., Kostic´, S., Pantovic´, R., & Vasovic´, N. (2014). Prediction of blast-produced ground motion in a copper mine. International Journal of Rock Mechanics and Mining Sciences,69, 19–25.CrossRefGoogle Scholar
  71. Rai, R., & Singh, T. N. (2004). A new predictor for ground vibration prediction and its comparison with other predictors. Indian Journal of Engineering and Materials Sciences,11(3), 178–184.Google Scholar
  72. Ren, F., Wu, X., Zhang, K., & Niu, R. (2015). Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China. Environmental Earth Sciences,73, 4791–4804.CrossRefGoogle Scholar
  73. Rezapour Tabari, M. M., & Zarif Sanayei, H. R. (2018). Prediction of the intermediate block displacement of the dam crest using artificial neural network and support vector regression models. Soft Computing,23(19), 9629–9645.CrossRefGoogle Scholar
  74. Roy, P. P. (1991). Vibration control in an opencast mine based on improved blast vibration predictors. Mining Science and Technology,12(2), 157–165.CrossRefGoogle Scholar
  75. Segarra, P., Domingo, J. F., López, A. M., Sanchidrián, J. A., & Orteg, M. F. (2010). Prediction of near field overpressure from quarry blasting. Applied Acoustics,71, 1169–1176.CrossRefGoogle Scholar
  76. Shahnazar, A., Rad, H. N., Hasanipanah, M., Tahir, M. M., Armaghani, D. J., & Ghoroqi, M. (2017). A new developed approach for the prediction of ground vibration using a hybrid PSO-optimized ANFIS-based model. Environmental Earth Sciences,76(15), 527.CrossRefGoogle Scholar
  77. Shahpoori Arani, K., Zandi, Y., Pham, B. T., Mu’azu, M. A., Katebi, J., Mohammadhassani, M., et al. (2019). Computational optimized finite element modelling of mechanical interaction of concrete with fiber reinforced polymer. Computers and Concrete,23, 061–68.Google Scholar
  78. 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. Natural Resources Research. Scholar
  79. Shirani Faradonbeh, R., Hasanipanah, M., Amnieh, H. B., Armaghani, D. J., & Monjezi, M. (2018). Development of GP and GEP models to estimate an environmental issue induced by blasting operation. Environmental Monitoring and Assessment,190(6), 351.CrossRefGoogle Scholar
  80. Shirani Faradonbeh, R., Jahed Armaghani, D., Bakhshandeh Amnieh, H., & Tonnizam Mohamad, E. (2016). Prediction and minimization of blast-induced flyrock using gene expression programming and firefly algorithm. Neural Computing and Applications. Scholar
  81. Shirzad, A., Tabesh, M., & Farmani, R. (2014). A comparison between performance of support vector regression and artificial neural network in prediction of pipe burst rate in water distribution networks. KSCE Journal of Civil Engineering,18(4), 941–948.CrossRefGoogle Scholar
  82. Taheri, K., Hasanipanah, M., Bagheri Golzar, S., & Abd Majid, M. Z. (2017). A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration. Engineering with Computers,33, 689–700.CrossRefGoogle Scholar
  83. Vapnik, V. N. (1998). Statistical learning theory (p. 740). New York: Wiley.Google Scholar
  84. Wu, C. H., Tzeng, G. H., Goo, Y. J., & Fang, W. C. (2007). A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy. Expert Systems with Applications,32(2), 397–408.CrossRefGoogle Scholar
  85. Xu, T. (2019). Blasting vibration safety criterion of surrounding rock of a circular tunnel. Geotechnical and Geological Engineering,37, 3077–3084.CrossRefGoogle Scholar
  86. Yang, X. S. (2009). Firefly algorithms for multimodal optimization. In Stochastic algorithms: Foundations and applications, SAGA 2009, lecture notes in computer science (Vol. 5792, pp. 169–178).CrossRefGoogle Scholar
  87. Yang, X. S. (2010a). Engineering optimization: An introduction with metaheuristic applications. New York: Wiley.CrossRefGoogle Scholar
  88. Yang, X. S. (2010b). Firefly algorithm, Levy flights and global optimization. In M. Bramer, et al. (Eds.), Research and development in intelligent systems XXVI (pp. 209–218). London: Springer.CrossRefGoogle Scholar
  89. Yang, X. S. (2010c). Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-Inspired Computation,2, 78–84.CrossRefGoogle Scholar
  90. Yang, X. S. (2010d). Nature-inspired metaheuristic algorithms. Beckington: Luniver Press.Google Scholar
  91. Yang, H., Hasanipanah, M., Tahir, M. M., & Bui, D. T. (2019). Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Natural Resources Research. Scholar
  92. Yang, H. Q., Li, Z., Jie, T. Q., & Zhang, Z. Q. (2018). Effects of joints on the cutting behavior of disc cutter running on the jointed rock mass. Tunnelling and Underground Space Technology,81, 112–120.CrossRefGoogle Scholar
  93. Yang, H. Q., Wang, H., & Zhou, X. (2016). Analysis on the damage behavior of mixed ground during TBM cutting process. Tunnelling and Underground Space Technology,57, 55–65.CrossRefGoogle Scholar
  94. Yang, H. Q., Zeng, Y. Y., Lan, Y. F., & Zhou, X. P. (2014). Analysis of the excavation damaged zone around a tunnel accounting for geostress and unloading. International Journal of Rock Mechanics and Mining Sciences,69, 59–66.CrossRefGoogle Scholar
  95. Yaochu, J., & Branke, J. (2005). Evolutionary optimization in uncertain environments—A survey. IEEE Transactions on Evolutionary Computation,9(3), 303–317.CrossRefGoogle Scholar
  96. Yoon, H., Jun, S. C., Hyun, Y., Bae, G. O., & Lee, K. K. (2011). A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. Journal of Hydrology,396, 128–138.CrossRefGoogle Scholar
  97. Zhang, X., Nguyen, H., Bui, X. N., Tran, Q. H., Nguyen, D. A., Tien Bui, D., et al. (2019). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural Resources Research. Scholar
  98. Zhou, X. P., & Yang, H. Q. (2007). Micromechanical modeling of dynamic compressive responses of mesoscopic heterogeneous brittle rock. Theoretical and Applied Fracture Mechanics,48(1), 1–20.CrossRefGoogle Scholar

Copyright information

© International Association for Mathematical Geosciences 2019

Authors and Affiliations

  1. 1.School of Civil EngineeringChongqing UniversityChongqingChina
  2. 2.College of Computer ScienceTabari University of BabolBabolIran
  3. 3.Institute of Research and DevelopmentDuy Tan UniversityDa NangVietnam
  4. 4.School of Mining, College of EngineeringUniversity of TehranTehranIran
  5. 5.Department of Computer Engineering, Mashhad BranchIslamic Azad UniversityMashhadIran

Personalised recommendations