Skip to main content

Advertisement

SpringerLink
Log in

Enhanced Prediction Model for Blast-Induced Air Over-Pressure in Open-Pit Mines Using Data Enrichment and Random Walk-Based Grey Wolf Optimization–Two-Layer ANN Model

  • Original Paper
  • Published:
Natural Resources Research Aims and scope Submit manuscript

Abstract

In this study, two innovative techniques were introduced, including data enrichment and optimization, with the aim of significantly improving the accuracy of air over-pressure (AOP) prediction models in mine blasting. Firstly, the Extra Trees algorithm was applied to enrich the collected dataset with the goal of enhancing the understanding of the predictive models for AOP prediction. Then, a neural network model with two hidden layers (ANN) was designed to predict AOP using both the original and enriched datasets. Secondly, to further enhance the accuracy of the ANN model, a novel optimization algorithm based on a random walk strategy and the grey wolf optimization algorithm (RWGWO) was employed to optimize the weights of the ANN model. This optimized model, referred to as the RWGWO–ANN model, was developed and evaluated for predicting AOP using both the original and enriched datasets. To comprehensively assess the impact of data enrichment and the proposed RWGWO-ANN model, three other optimization algorithms—particle swarm optimization (PSO), fruit-fly optimization algorithm (FOA), and single-based genetic algorithm (SGA)—were also applied to optimize the ANN model for AOP prediction. These models were named PSO–ANN, FOA–ANN, and SGA–ANN, respectively. The tenfold cross-validation procedure was applied and repeated three times to ensure the objectivity and consistency of the models. Additionally, conventional ANN and the United States Bureau of Mines empirical model were developed for comparison, serving similar purposes to evaluate the efficiency of the optimization algorithms employed in this study. To demonstrate the advantages of the proposed method and models, a dataset comprising 312 blasting events and six input parameters at the Coc Sau open-pit coal mine in Vietnam was gathered and analyzed. These parameters included burden, spacing, rock hardness, powder factor, monitoring distance, and maximum explosive charge per delay. An additional input variable—Extra Trees—was introduced, making the total number of input variables seven in the enriched dataset. The proposed hybrid model, along with others, was developed based on both the original and enriched datasets. The results revealed that the Extra Trees algorithm is robust and effectively enriches the raw dataset, enhancing the understanding of predictive models and providing improved accuracy. Sensitivity analysis results also highlighted the robust contribution of the Extra Trees variable in the enriched dataset. Compared to the original dataset, the performance of AOP predictive models was improved by 7–24% using the enriched dataset enriched by the Extra Trees algorithm. Furthermore, the findings indicated that the RWGWO–ANN model exhibited the highest accuracy in predicting AOP in this study, achieving an accuracy of 96.2%. This marked a 16–20% improvement over the accuracy of the conventional ANN model.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure 21
Figure 22
Figure 23
Figure 24
Figure 25

Similar content being viewed by others

References

  • Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 1–74.

    Article  Google Scholar 

  • Amiri, M., Bakhshandeh Amnieh, H., Hasanipanah, M., & Mohammad Khanli, L. (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, 631–644.

    Article  Google Scholar 

  • Armaghani, D. J., Hajihassani, M., Marto, A., Faradonbeh, R. S., & Mohamad, E. T. (2015a). Prediction of blast-induced air overpressure: A hybrid AI-based predictive model. Environmental Monitoring and Assessment, 187, 1–13.

    Google Scholar 

  • Armaghani, D. J., Hajihassani, M., Sohaei, H., Mohamad, E. T., Marto, A., Motaghedi, H., & Moghaddam, M. R. (2015b). Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arabian Journal of Geosciences, 8(12), 10937–10950.

    Article  Google Scholar 

  • Armaghani, D. J., Hasanipanah, M., & Mohamad, E. T. (2016). A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Engineering with Computers, 32(1), 155–171.

    Article  Google Scholar 

  • Azad, S. K., Azad, S. K., & Kulkarni, A. J. (2012). Structural optimization using a mutation-based genetic algorithm. International Journal of Optimization in Civil Engineering, 2(1), 80–100.

    Google Scholar 

  • Baghbani, A., Choudhury, T., Costa, S., & Reiner, J. (2022). Application of artificial intelligence in geotechnical engineering: A state-of-the-art review. Earth-Science Reviews, 228, 103991.

    Article  Google Scholar 

  • Bai, Y., Gautam, T., & Sojoudi, S. (2023). Efficient global optimization of two-layer relu networks: Quadratic-time algorithms and adversarial training. SIAM Journal on Mathematics of Data Science, 5(2), 446–474.

    Article  MathSciNet  Google Scholar 

  • Bansal, J. C. (2019). Particle Swarm Optimization (pp. 11–23). Berlin: Springer.

    Google Scholar 

  • Bensingh, R. J., Machavaram, R., Boopathy, S. R., & Jebaraj, C. (2019). Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO). Measurement, 134, 359–374.

    Article  ADS  Google Scholar 

  • Bui, X.-N., Bui, H.-B., & Nguyen H. (2021). A review of artificial intelligence applications in mining and geological engineering. In Proceedings of the international conference on innovations for sustainable and responsible mining: ISRM 2020-Vol. 1, Springer. Berlin

  • Chao, Z., Ma, G., Zhang, Y., Zhu, Y., & Hu, H. (2018). The application of artificial neural network in geotechnical engineering. In IOP conference series: Earth and environmental science. IOP Publishing.

  • de Paz-Centeno, I., García-Ordás, M. T., García-Olalla, Ó., & Alaiz-Moretón, H. (2023). Imputation of missing measurements in PV production data within constrained environments. Expert Systems with Applications, 217, 119510.

    Article  Google Scholar 

  • Dramsch, J. S. (2020). 70 years of machine learning in geoscience in review. Advances in Geophysics, 61, 1–55.

    Article  ADS  PubMed Central  Google Scholar 

  • Falco, I. D., Cioppa, A. D., & Tarantino, E. (2002). Mutation-based genetic algorithm: Performance evaluation. Applied Soft Computing, 1(4), 285–299.

    Article  Google Scholar 

  • García, S., Luengo, J., & Herrera, F. (2015). Data preprocessing in data mining. Berlin: Springer.

    Book  Google Scholar 

  • Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.

    Article  Google Scholar 

  • Gómez-Iglesias, A., Vega-Rodríguez, M. A., Castejón-Magaña, F., Cárdenas-Montes, M., & Morales-Ramos, E. (2009). Grid-enabled mutation-based genetic algorithm to optimise nuclear fusion devices. In Computer aided systems theory-EUROCAST 2009: 12th international conference, Las Palmas de Gran Canaria, Spain, February 15–20, 2009, Revised selected papers 12. Springer, Berlin.

  • Guo, H., Li, H., Xiong, J., & Yu, M. (2019). Indoor positioning system based on particle swarm optimization algorithm. Measurement, 134, 908–913.

    Article  ADS  Google Scholar 

  • Gupta, S., & Deep, K. (2019). A novel random walk grey wolf optimizer. Swarm and Evolutionary Computation, 44, 101–112.

    Article  Google Scholar 

  • Han, K., & Wang, Y. (2021). A review of artificial neural network techniques for environmental issues prediction. Journal of Thermal Analysis and Calorimetry, 145(4), 2191–2207.

    Article  CAS  Google Scholar 

  • Harandizadeh, H., & Armaghani, D. J. (2021). Prediction of air-overpressure induced by blasting using an ANFIS-PNN model optimized by GA. Applied Soft Computing, 99, 106904.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • He, Z., Armaghani, D. J., Masoumnezhad, M., Khandelwal, M., Zhou, J., & Murlidhar, B. R. (2021). A combination of expert-based system and advanced decision-tree algorithms to predict air–overpressure resulting from quarry blasting. Natural Resources Research, 30, 1889–1903.

    Article  Google Scholar 

  • Hosseini, S., Monjezi, M., Bakhtavar, E., & Mousavi, A. (2021). Prediction of dust emission due to open pit mine blasting using a hybrid artificial neural network. Natural Resources Research, 30(6), 4773–4788.

    Article  Google Scholar 

  • John, V., Liu, Z., Guo, C., Mita, S., & Kidono, K. (2015). Real-time lane estimation using deep features and extra trees regression. Berlin: Springer.

    Google Scholar 

  • Kan, J. (2017). Evaluation of mining engineering technology innovation ability and application based on BP neural network. In 2017 6th international conference on industrial technology and management (ICITM), IEEE.

  • Eberhart & J. Kennedy (1995). A new optimizer using particle swarm theory. MHS'95. In Proceedings of the sixth international symposium on micro machine and human science, IEEE.

  • Kennedy, J. (2011). Particle swarm optimization (pp. 760–766). Berlin: Springer.

    Google Scholar 

  • Lau, T., & Tsang, E. P. (1997). Solving the processor configuration problems with a mutation-based genetic algorithm. International Journal on Artificial Intelligence Tools, 6(04), 567–585.

    Article  Google Scholar 

  • Li, S., & Zheng, D. (2003). Applications of artificial neural networks to geosciences: Review and prospect. Advances in Earth Science, 18(1), 68.

    MathSciNet  Google Scholar 

  • Liu, Z., Peng, C., Xiang, W., Tian, D., Deng, X., & Zhao, M. (2010). Application of artificial neural networks in global climate change and ecological research: An overview. Chinese Science Bulletin, 55, 3853–3863.

    Article  ADS  Google Scholar 

  • Maier, O., Wilms, M., von der Gablentz, J., Krämer, U. M., Münte, T. F., & Handels, H. (2015). Extra tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences. Journal of Neuroscience Methods, 240, 89–100.

    Article  PubMed  Google Scholar 

  • 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, 1–15.

    Google Scholar 

  • Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2), 161–174.

    Article  Google Scholar 

  • Murlidhar, B. R., Nguyen, H., Rostami, J., Bui, X., Armaghani, D. J., Ragam, P., & Mohamad, E. T. (2021). Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network. Journal of Rock Mechanics and Geotechnical Engineering, 13(6), 1413–1427.

    Article  Google Scholar 

  • Nadi, A., Tayarani-Bathaie, S., & Safabakhsh, R. (2009). Evolution of neural network architecture and weights using mutation based genetic algorithm. In 2009 14th international CSI computer conference, IEEE.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  MathSciNet  Google Scholar 

  • Nguyen, H., Bui, X.-N., Tran, Q.-H., Van Hoa, P., Nguyen, D.-A., Hoa, L. T. T., Le, Q.-T., Do, N.-H., Bao, T. D., & Bui, H.-B. (2020). A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophysica, 68, 325–336.

    Article  ADS  Google Scholar 

  • Pedram, M., Mousavirad, S. J., & Schaefer, G. (2022). Training neural networks with Lévy flight distribution algorithm. In Proceedings of 7th international conference on harmony search, soft computing and applications: ICHSA 2022, Springer, Berlin

  • Protodiakonov, M., Koifman, M., Chirkov, S., Kuntish, M., & Tedder, R. (1964). Rock strength passports and methods for their determination. Moscow: Nauka.

    Google Scholar 

  • Ramesh Murlidhar, B., Yazdani Bejarbaneh, B., Jahed Armaghani, D., Mohammed, A. S., & Tonnizam Mohamad, E. (2021). Application of tree-based predictive models to forecast air overpressure induced by mine blasting. Natural Resources Research, 30(2), 1865–1887.

    Article  Google Scholar 

  • Refaat, M. (2010). Data preparation for data mining using SAS. Amsterdam: Elsevier.

    Google Scholar 

  • 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 and Geotechnical Engineering, 6(1), 67–76.

    Article  Google Scholar 

  • Saeed, U., Jan, S. U., Lee, Y.-D., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering & System Safety, 205, 107284.

    Article  Google Scholar 

  • Sreejith, S., Nehemiah, H. K., & Kannan, A. (2020). A classification framework using a diverse intensified strawberry optimized neural network (DISON) for clinical decision-making. Cognitive Systems Research, 64, 98–116.

    Article  Google Scholar 

  • Tadeusiewicz, R. (2015). Neural networks in mining sciences–general overview and some representative examples. Archives of Mining Sciences, 60(4), 971–984.

    Article  ADS  Google Scholar 

  • Tran, Q.-H., Nguyen, H., & Bui, X.-N. (2023). Novel soft computing model for predicting blast-induced ground vibration in open-pit mines based on the bagging and sibling of extra trees models. Computer Modeling in Engineering & Sciences, 134(3), 2227–2246.

    Article  Google Scholar 

  • Xing, B., & Gao, W.-J. (2014). Fruit fly optimization algorithm. In Innovative computational intelligence: A rough guide to 134 clever algorithms (pp. 167–170).

  • Ye, J., Dalle, J., Nezami, R., Hasanipanah, M., & Armaghani, D. J. (2022). Stochastic fractal search-tuned ANFIS model to predict blast-induced air overpressure. Engineering with Computers, 38(1), 497–511.

    Article  Google Scholar 

  • Zhang, R., Li, Y., Gui, Y., & Zhou, J. (2022). Prediction of blasting induced air-overpressure using a radial basis function network with an additional hidden layer. Applied Soft Computing, 127, 109343.

    Article  Google Scholar 

  • Zhang, S., Carranza, E. J. M., Wei, H., Xiao, K., Yang, F., Xiang, J., Zhang, S., & Xu, Y. (2021). Data-driven mineral prospectivity mapping by joint application of unsupervised convolutional auto-encoder network and supervised convolutional neural network. Natural Resources Research, 30, 1011–1031.

    Article  Google Scholar 

Download references

Acknowledgments

Dr. Hoang Nguyen was funded by the Postdoctoral Scholarship Programme of Vingroup Innovation Foundation (VINIF), code VINIF.2022.STS.23.

Author information

Authors and Affiliations

  1. Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Bac Tu Liem District, 18 Vien Str., Duc Thang Ward, Hanoi, 100000, Vietnam

    Hoang Nguyen & Xuan-Nam Bui

  2. Institute of Mining and Special Engineering, Freiberg University of Mining and Technology, Gustav-Zeuner Strasse 1A, 09599, Freiberg, Sachsen, Germany

    Carsten Drebenstedt

  3. Innovations for Sustainable and Responsible Mining (ISRM) Research Group, Hanoi University of Mining and Geology, Bac Tu Liem District, 18 Vien Str., Duc Thang Ward, Hanoi, 100000, Vietnam

    Hoang Nguyen, Xuan-Nam Bui & Carsten Drebenstedt

  4. Department of Energy Resources Engineering, Pukyong National University, Busan, 48513, Korea

    Yosoon Choi

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. Carsten Drebenstedt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yosoon Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hoang Nguyen.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflicts of interest.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 34 kb)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nguyen, H., Bui, XN., Drebenstedt, C. et al. Enhanced Prediction Model for Blast-Induced Air Over-Pressure in Open-Pit Mines Using Data Enrichment and Random Walk-Based Grey Wolf Optimization–Two-Layer ANN Model. Nat Resour Res 33, 943–972 (2024). https://doi.org/10.1007/s11053-023-10299-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11053-023-10299-w

Keywords

Search

Navigation