Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., et al. (2021) A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion.
Ak, H., Iphar, M., Yavuz, M., & Konuk, A. (2009) Evaluation of ground vibration effect of blasting operations in a magnesite mine. Soil Dynamics Earthquake Engineering, 29(4), 669-676.
Ak, H., & Konuk, A. (2008) The effect of discontinuity frequency on ground vibrations produced from bench blasting: a case study. Soil Dynamics Earthquake Engineering, 28(9), 686-694.
Ambraseys, N. N., & Hendron, A. (1968). "Dynamic behaviour of rock masses", J. Wiley & Sons.
Amiri, M., Amnieh, H. B., Hasanipanah, M., & Khanli, L. M. (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Engineering with Computers, 32(4), 631-644.
Armaghani, D. J., Momeni, E., Abad, S. V. A. N. K., & Khandelwal, M. (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environmental Earth Sciences, 74(4), 2845-2860.
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.
Bayat, P., Monjezi, M., Rezakhah, M., & Armaghani, D. J. (2020) Artificial neural network and firefly algorithm for estimation and minimization of ground vibration induced by blasting in a mine. Natural resources research, 29, 4121-4132.
Bui, X.-N., Jaroonpattanapong, P., Nguyen, H., Tran, Q.-H., & Long, N. Q. (2019) 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., Tran, Q.-H., Nguyen, D.-A., & Bui, H.-B. (2021) Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization. Natural resources research, 30(3), 2663-2685.
Chen, W., Hasanipanah, M., Nikafshan Rad, H., Jahed Armaghani, D., & Tahir, M. (2021) A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration. Engineering with Computers, 37(2), 1455-1471.
Cheng, B., & Titterington, D. M. (1994) Neural networks: A review from a statistical perspective. Statistical science, 2-30.
Dindarloo, S. (2015) Peak particle velocity prediction using support vector machines: a surface blasting case study. Journal of the Southern African Institute of Mining Metallurgy, 115(7), 637-643.
Ding, Z., Nguyen, H., Bui, X.-N., Zhou, J., & Moayedi, H. (2020) 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, 29(2), 751-769.
Dorjsembe, U., Lee, J. H., Choi, B., & Song, J. W. (2021) Sparsity Increases Uncertainty Estimation in Deep Ensemble. Computers & Geosciences, 10(4), 54.
Duvall, W. I., & Fogelson, D. E. (1962). "Review of criteria for estimating damage to residences from blasting vibrations", (Vol. 5968), US Department of the Interior, Bureau of Mines.
Faradonbeh, R. S., Armaghani, D. J., Abd Majid, M., Tahir, M. M., Murlidhar, B. R., Monjezi, M., et al. (2016) Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction. International journal of environmental science technology, 13(6), 1453-1464.
Fişne, A., Kuzu, C., Hüdaverdi, T., & assessment. (2011) Prediction of environmental impacts of quarry blasting operation using fuzzy logic. Environmental monitoring, 174(1), 461-470.
Gal, Y., & Ghahramani, Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. international conference on machine learning, 2016 (pp. 1050-1059): PMLR
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., et al. (2021) A survey of uncertainty in deep neural networks. arXiv preprint arXiv:.03342.
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 Control, 19(5), 755-770.
Ghoraba, S., Monjezi, M., Talebi, N., Armaghani, D. J., & Moghaddam, M. (2016) Estimation of ground vibration produced by blasting operations through intelligent and empirical models. Environmental Earth Sciences, 75(15), 1-9.
Ghosh, A., & Daemen, J. A simple new blast vibration predictor of ground vibrations induced predictor. Proceedings of the 24th US symposium on rock mechanics. Texas, 1983
Goodfellow, I. J., Shlens, J., & Szegedy, C. (2014) Explaining and harnessing adversarial examples. arXiv.
Haghnejad, A., Ahangari, K., Moarefvand, P., & Goshtasbi, K. (2018) Numerical investigation of the impact of geological discontinuities on the propagation of ground vibrations. Geomech Eng, 14(6), 545-552.
Hajihassani, M., Armaghani, D. J., Marto, A., & Mohamad, E. T. (2015) Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology the Environment, 74(3), 873-886.
Hammed, O., Popoola, O., Adetoyinbo, A., Awoyemi, M., Adagunodo, T., Olubosede, O., et al. (2018) Peak particle velocity data acquisition for monitoring blast induced earthquakes in quarry sites. Data in brief, 19, 398-408.
Hasanipanah, M., Faradonbeh, R. S., Amnieh, H. B., Armaghani, D. J., & Monjezi, M. (2017a) Forecasting blast-induced ground vibration developing a CART model. Engineering with Computers, 33(2), 307-316.
Hasanipanah, M., Monjezi, M., Shahnazar, A., Armaghani, D. J., & Farazmand, A. (2015) Feasibility of indirect determination of blast induced ground vibration based on support vector machine. Measurement, 75, 289-297.
Hasanipanah, M., Naderi, R., Kashir, J., Noorani, S. A., & Qaleh, A. Z. A. (2017b) Prediction of blast-produced ground vibration using particle swarm optimization. Engineering with Computers, 33(2), 173-179.
Haykin, S. S. (2009) Neural networks and learning machines. New York: Prentice Hall.
Huang, J., Koopialipoor, M., & Armaghani, D. J. (2020) A combination of fuzzy Delphi method and hybrid ANN-based systems to forecast ground vibration resulting from blasting. Scientific Reports, 10(1), 1-21.
Iphar, M., Yavuz, M., & Ak, H. (2008) Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environmental geology, 56(1), 97-107.
Kanevski, M., Pozdnoukhov, A., Pozdnukhov, A., & Timonin, V. (2009). "Machine learning for spatial environmental data: theory, applications, and software", EPFL press.
Khandelwal, M., Armaghani, D. J., Faradonbeh, R. S., Yellishetty, M., Abd Majid, M. Z., & Monjezi, M. (2017) Classification and regression tree technique in estimating peak particle velocity caused by blasting. Engineering with Computers, 33(1), 45-53.
Khandelwal, M., Kankar, P., & Harsha, S. (2010) Evaluation and prediction of blast induced ground vibration using support vector machine. Mining Science Technology, 20(1), 64-70.
Khandelwal, M., Kumar, D. L., & Yellishetty, M. (2011) Application of soft computing to predict blast-induced ground vibration. Engineering with Computers, 27(2), 117-125.
Khosravi, A., Nahavandi, S., Creighton, D., & Atiya, A. F. (2011) Comprehensive review of neural network-based prediction intervals and new advances. IEEE Transactions on neural networks, 22(9), 1341-1356.
Kraus, M., Feuerriegel, S., & Oztekin, A. (2020) Deep learning in business analytics and operations research: Models, applications and managerial implications. European journal of operational research, 281(3), 628-641.
Kumar, R., Choudhury, D., & Bhargava, K. (2014) Prediction of blast-induced vibration parameters for soil sites. International Journal of Geomechanics, 14(3), 04014007.
Lakshminarayanan, B., Pritzel, A., & Blundell, C. (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Advances in neural information processing systems, 30.
Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D. (2022) Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sensing of Environment, 268, 112760.
Langefors, U., & Kihlstrom, B. (1978). "The modern techniques of rock blasting". New York, Willey.
Mohamadnejad, M., Gholami, R., & Ataei, M. (2012) Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations. Tunnelling Underground Space Technology, 28, 238-244.
Monjezi, M., Baghestani, M., Faradonbeh, R. S., Saghand, M. P., & Armaghani, D. J. (2016) Modification and prediction of blast-induced ground vibrations based on both empirical and computational techniques. Engineering with Computers, 32(4), 717-728.
Monjezi, M., Ghafurikalajahi, M., & Bahrami, A. (2011) Prediction of blast-induced ground vibration using artificial neural networks. Tunnelling Underground Space Technology, 26(1), 46-50.
Murlidhar, B. R., Kumar, D., Armaghani, D. J., Mohamad, E. T., Roy, B., & Pham, B. T. (2020) A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock. Natural resources research, 29(6), 4103-4120.
Nguyen, H., Bui, X.-N., Bui, H.-B., & Cuong, D. T. (2019) 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., Tran, Q.-H., Nguyen, D.-A., Hoa, L. T. T., Le, Q.-T., et al. (2021) Predicting blast-induced ground vibration in open-pit mines using different nature-inspired optimization algorithms and deep neural network. Natural resources research, 1-23.
Nguyen, H., Drebenstedt, C., Bui, X.-N., & Bui, D. T. (2020) 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, 29(2), 691-709.
Papadopoulos, G., Edwards, P. J., & Murray, A. F. (2001) Confidence estimation methods for neural networks: A practical comparison. IEEE transactions on neural networks, 12(6), 1278-1287.
Pearce, T., Brintrup, A., Zaki, M., & Neely, A. High-quality prediction intervals for deep learning: A distribution-free, ensembled approach. International Conference on Machine Learning, 2018 (pp. 4075-4084): PMLR
Roy, P. P. (1991) Vibration control in an opencast mine based on improved blast vibration predictors. Mining Science Technology, 12(2), 157-165.
Saadat, M., Khandelwal, M., & Monjezi, M. (2014) An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran. Journal of Rock Mechanics Geotechnical Engineering, 6(1), 67-76.
Shahnazar, A., Rad, H. N., Hasanipanah, M., Tahir, 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), 1-17.
Shang, Y., Nguyen, H., Bui, X.-N., Tran, Q.-H., & Moayedi, H. (2020) 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, 29(2), 723-737.
Sheykhi, H., Bagherpour, R., Ghasemi, E., & Kalhori, H. (2018) Forecasting ground vibration due to rock blasting: a hybrid intelligent approach using support vector regression and fuzzy C-means clustering. Engineering with Computers, 34(2), 357-365.
Simangunsong, G. M., & Wahyudi, S. (2015) Effect of bedding plane on prediction blast-induced ground vibration in open pit coal mines. International Journal of Rock Mechanics Mining Sciences, 100(79), 1-8.
Singh, T., & Singh, V. (2005a) An intelligent approach to prediction and control ground vibration in mines. Geotechnical geological engineering, 23(3), 249-262.
Singh, T., & Singh, V. (2005b) An intelligent approach to prediction and control ground vibration in mines. Geotechnical
Geological Engineering, 23(3), 249-262.
Taheri, K., Hasanipanah, M., Golzar, S. B., & 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(3), 689-700.
Trippi, R. R., & Turban, E. (1992). "Neural networks in finance and investing: Using artificial intelligence to improve real world performance", McGraw-Hill, Inc.
Uyar, G. G., & Aksoy, C. (2019) Comparative review and interpretation of the conventional and new methods in blast vibration analyses. Geomechanics Engineering with Computers, 18(5), 545-554.
Xue, X. (2019) Neuro-fuzzy based approach for prediction of blast-induced ground vibration. Applied Acoustics, 152, 73-78.
Yang, H., Hasanipanah, M., Tahir, M., & Bui, D. T. (2020) Intelligent prediction of blasting-induced ground vibration using ANFIS optimized by GA and PSO. Natural resources research, 29(2), 739-750.
Yu, Z., Shi, X., Zhou, J., Chen, X., & Qiu, X. (2020) Effective assessment of blast-induced ground vibration using an optimized random forest model based on a Harris hawks optimization algorithm. Applied Sciences, 10(4), 1403.
Zhang, H., Zhou, J., Jahed Armaghani, D., Tahir, M., Pham, B. T., & Huynh, V. V. (2020) A combination of feature selection and random forest techniques to solve a problem related to blast-induced ground vibration. Applied Sciences, 10(3), 869.
Zhang, X., Nguyen, H., Bui, X.-N., Tran, Q.-H., Nguyen, D.-A., Bui, D. T., et al. (2020) Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on particle swarm optimization and XGBoost. Natural resources research, 29(2), 711-721.
Zhou, J., Asteris, P. G., Armaghani, D. J., & Pham, B. T. (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dynamics Earthquake Engineering, 139, 106390.
Zhu, X., Vondrick, C., Fowlkes, C. C., & Ramanan, D. (2016) Do we need more training data? International Journal of Computer Vision, 119(1), 76-92.