Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-70939-w.pdf
Reference48 articles.
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2. Taiwo, B. O. et al. Artificial neural network modeling as an approach to limestone blast production rate prediction: A comparison of PI-BANN and MVR models. J. Min. Environ. 14(2), 375–388. https://doi.org/10.22044/jme.2023.12489.2266 (2023).
3. Y. Fissha, H. Ikeda, H. Toriya, N. Owada, T. Adachi, & Y. Kawamura. Evaluation and prediction of blast-induced ground vibrations: A Gaussian Process Regression (GPR) Approach. 659–682 (2023).
4. Zhou, J., Li, C., Koopialipoor, M., Armaghani, D. J. & Pham, B. T. Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP). Int. J. Mining Reclam. Environ. 35(1), 48–68. https://doi.org/10.1080/17480930.2020.1734151 (2021).
5. H. Zhang, J. Zhou, D. J. Armaghani, M. M. Tahir, & B. T. Pham. Applied sciences A combination of feature selection and random forest techniques to solve a problem related to. Appl. Sci. (2020).
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