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Year 2020
 
Vol 61, Issue 6, [12 - 2020] - Full papers

Application of the k - nearest neighbors algorithm for predicting blast - induced ground vibration in open - pit coal mines: a case study

DOI:10.46326/JMES.2020.61(6).03

  • Affiliations:

    1 Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Vietnam
    2 Center for Mining, Electro - Mechanical Research, Hanoi University of Mining and Geology, Vietnam

  • *Corresponding:
    This email address is being protected from spambots. You need JavaScript enabled to view it.
  • Received: 15th-Aug-2020
  • Revised: 5th-Dec-2020
  • Accepted: 31st-Dec-2020
  • Online: 31st-Dec-2020
Pages: 22 - 29
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Cited by: 10

Abstract:

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.

How to Cite
., H.Nguyen 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 (Dec, 2020), 22-29. DOI:https://doi.org/10.46326/JMES.2020.61(6).03.
References

Armaghani, D. J., Hajihassani, M., Mohamad, E. T., Marto, A. and Noorani, S., (2014). Blasting - induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arabian Journal of Geosciences, 7(12): 5383 - 5396.

Armaghani, D. J., Hasanipanah, M., Amnieh, H. B. and Mohamad, E. T., (2018). Feasibility of ICA in approximating ground vibration resulting from mine blasting. Neural Computing and Applications, 29(9): 457 - 465.

Armaghani, D. J., Momeni, E., Abad, S. V. A. N. K. and Khandelwal, M., (2015). Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environmental Earth Sciences, 74(4): 2845 - 2860.

Atkeson, C. G., Moore, A. W. and Schaal, S., (1997). Locally weighted learning for control. Lazy learning, Springer: 75 - 113.

Bui, X. - N., Choi, Y., Atrushkevich, V., Nguyen, H., Tran, Q. - H., Long, N. Q. and Hoang, H. - T., (2020). Prediction of Blast - Induced Ground Vibration Intensity in Open - Pit Mines Using Unmanned Aerial Vehicle and a Novel Intelligence System. Natural Resources Research, 29(2): 771 - 790, DOI: 10.1007/s11053 - 019 - 09573 - 7.

Bui, X. - N., Nguyen, H., Le, H. - A., Bui, H. - B. and Do, N. - H., (2019). Prediction of Blast - induced Air Over - pressure in Open - Pit Mine: Assessment of Different Artificial Intelligence Techniques. Natural Resources Research, 29(2): 571 - 591, DOI: 10.1007/s11053 - 019 - 09461 - 0.

Ding, Z., Nguyen, H., Bui, X. - N., Zhou, J. and 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, DOI: 10.1007/s11053 - 019 - 09548 - 8.

Duvall, W. I. and Petkof, B., (1958). Spherical propagation of explosion - generated strain pulses in rock, Bureau of Mines

Fang, Q., Nguyen, H., Bui, X. - N. and Nguyen - Thoi, T., (2019a). Prediction of Blast - Induced Ground Vibration in Open - Pit Mines Using a New Technique Based on Imperialist Competitive Algorithm and M5Rules. Natural Resources Research, 29(2): 791 - 806, DOI: 10.1007/s11053 - 019 - 09577 - 3.

Fang, Q., Nguyen, H., Bui, X. - N. and Tran, Q. - H., (2019b). Estimation of Blast - Induced Air Overpressure in Quarry Mines Using Cubist - Based Genetic Algorithm. Natural Resources Research, 29(2): 593 - 607, DOI: 10.1007/s11053 - 019 - 09575 - 5.

Gou, J., Ma, H., Ou, W., Zeng, S., Rao, Y. and Yang, H., (2019). A generalized mean distance - based k - nearest neighbor classifier. Expert Systems with Applications, 115: 356 - 372.

Hajihassani, M., Armaghani, D. J., Marto, A. and Mohamad, E. T., (2015). 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.

Härdle, W. and Linton, O., (1994). Applied nonparametric methods. Handbook of econometrics, 4: 2295 - 2339.

Hasanipanah, Faradonbeh, Amnieh, Armaghani and Monjezi, (2017). Forecasting blast - induced ground vibration developing a CART model. Engineering with Computers, 33(2): 307 - 316.

Khandelwal, M., (2011). Blast - induced ground vibration prediction using support vector machine. Engineering with Computers, 27(3): 193 - 200.

Monjezi, M., Ahmadi, M., Sheikhan, M., Bahrami, A. and Salimi, A., (2010). Predicting blast - induced ground vibration using various types of neural networks. Soil Dynamics and Earthquake Engineering, 30(11): 1233 - 1236.

Monjezi, M., Hasanipanah, M. and 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.

Nguyen, H., (2019). Support vector regression approach with different kernel functions for predicting blast - induced ground vibration: a case study in an open - pit coal mine of Vietnam. SN Applied Sciences, 1(4): 283.

Nguyen, H. and 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, DOI: 10.1007/s11053 - 018 - 9424 - 1.

Nguyen, H., Bui, X. - N., Choi, Y., Lee, C. W. and Armaghani, D. J., (2020). A Novel Combination of Whale Optimization Algorithm and Support Vector Machine with Different Kernel Functions for Prediction of Blasting - Induced Fly - Rock in Quarry Mines. Natural Resources Research, DOI: 10.1007/s11053 - 020 - 09710 - 7.

Nguyen, H., Bui, X. - N., Tran, Q. - H., Le, T. - Q. and Do, N. - H., (2019a). Evaluating and predicting blast - induced ground vibration in open - cast mine using ANN: A case study in Vietnam. SN Applied Sciences, 1(1): 125.

Nguyen, H., Drebenstedt, C., Bui, X. - N. and 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, 29(2): 691 - 709.

Peterson, L. E., (2009). K - nearest neighbor. Scholarpedia, 4(2): 1883.

Shang, Y., Nguyen, H., Bui, X. - N., Tran, Q. - H. and 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, 29(2): 723 - 737, DOI: 10.1007/s11053 - 019 - 09503 - 7.

Sun, J., Bo, Y., Luo, J. and Yang, J., (2019). Application of the K Nearest Neighbor Algorithm Based on Scaling Weight in Intelligent Attendance System. 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), IEEE.

Syaliman, K., Nababan, E. and Sitompul, O., (2018). Improving the accuracy of k - nearest neighbor using local mean based and distance weight. Journal of Physics: Conference Series.

Yang, H., Hasanipanah, M., Tahir, M. M. and Bui, D. T., (2019). Intelligent Prediction of Blasting - Induced Ground Vibration Using ANFIS Optimized by GA and PSO. Natural Resources Research, 29(2): 739 - 750, DOI: 10.1007/s11053 - 019 - 09515 - 3.

Zhang, X., Nguyen, H., Bui, X. - N., Tran, Q. - H., Nguyen, D. - A., Bui, D. T. and Moayedi, H., (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, 29(2): 711 - 721, DOI: 10.1007/s11053 - 019 - 09492 - 7.

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