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Estimating Flyrock Distance Induced Due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer

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  • Ramesh Murlidhar Bhatawdekar

    (Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Radhikesh Kumar

    (Department of Computer Science and Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna 800005, India)

  • Mohanad Muayad Sabri Sabri

    (Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Bishwajit Roy

    (School of Computer Science, University of Petroleum and Energy Studies (UPES), Dehradun 248007, India)

  • Edy Tonnizam Mohamad

    (Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Deepak Kumar

    (Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna 800005, India)

  • Sangki Kwon

    (Department of Energy Resources Engineering, Inha University, Yong-Hyun Dong, Nam Ku, Incheon 402-751, Republic of Korea)

Abstract

Blasting is essential for breaking hard rock in opencast mines and tunneling projects. It creates an adverse impact on flyrock. Thus, it is essential to forecast flyrock to minimize the environmental effects. The objective of this study is to forecast/estimate the amount of flyrock produced during blasting by applying three creative composite intelligent models: equilibrium optimizer-coupled extreme learning machine (EO-ELM), particle swarm optimization-based extreme learning machine (PSO-ELM), and particle swarm optimization-artificial neural network (PSO-ANN). To obtain a successful conclusion, we considered 114 blasting data parameters consisting of eight inputs (hole diameter, burden, stemming length, rock density, charge-per-meter, powder factor (PF), blastability index (BI), and weathering index), and one output parameter (flyrock distance). We then compared the results of different models using seven different performance indices. Every predictive model accomplished the results comparable with the measured values of flyrock. To show the effectiveness of the developed EO-ELM, the result from each model run 10-times is compared. The average result shows that the EO-ELM model in testing (R2 = 0.97, RMSE = 32.14, MAE = 19.78, MAPE = 20.37, NSE = 0.93, VAF = 93.97, A20 = 0.57) achieved a better performance as compared to the PSO-ANN model (R2 = 0.87, RMSE = 64.44, MAE = 36.02, MAPE = 29.96, NSE = 0.72, VAF = 74.72, A20 = 0.33) and PSO-ELM model (R2 = 0.88, RMSE = 48.55, MAE = 26.97, MAPE = 26.71, NSE = 0.84, VAF = 84.84, A20 = 0.51). Further, a non-parametric test is performed to assess the performance of these three models developed. It shows that the EO-ELM performed better in the prediction of flyrock compared to PSO-ELM and PSO-ANN. We did sensitivity analysis by introducing a new parameter, WI. Input parameters, PF and BI, showed the highest sensitivity with 0.98 each.

Suggested Citation

  • Ramesh Murlidhar Bhatawdekar & Radhikesh Kumar & Mohanad Muayad Sabri Sabri & Bishwajit Roy & Edy Tonnizam Mohamad & Deepak Kumar & Sangki Kwon, 2023. "Estimating Flyrock Distance Induced Due to Mine Blasting by Extreme Learning Machine Coupled with an Equilibrium Optimizer," Sustainability, MDPI, vol. 15(4), pages 1-26, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3265-:d:1064821
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    References listed on IDEAS

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    1. Liu, Hui & Mi, Xiwei & Li, Yanfei, 2018. "An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm," Renewable Energy, Elsevier, vol. 123(C), pages 694-705.
    2. Radhikesh Kumar & Maheshwari Prasad Singh & Bishwajit Roy & Afzal Hussain Shahid, 2021. "A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1927-1960, April.
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    1. Andrzej Biessikirski & Przemysław Bodziony & Michał Dworzak, 2024. "Energy Consumption and Fume Analysis: A Comparative Analysis of the Blasting Technique and Mechanical Excavation in a Polish Gypsum Open-Pit Mine," Energies, MDPI, vol. 17(22), pages 1-28, November.

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