Comparative performance of six supervised learning methods for the development of models of hard rock pillar stability prediction
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DOI: 10.1007/s11069-015-1842-3
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- Zaobao Liu & Jianfu Shao & Weiya Xu & Yongdong Meng, 2013. "Prediction of rock burst classification using the technique of cloud models with attribution weight," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(2), pages 549-568, September.
- Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
- Zaobao Liu & Jianfu Shao & Weiya Xu & Hongjie Chen & Yu Zhang, 2014. "An extreme learning machine approach for slope stability evaluation and prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 73(2), pages 787-804, September.
- Rafael Pino-Mejias & Mercedes Carrasco-Mairena & Antonio Pascual-Acosta & Maria-Dolores Cubiles-De-La-Vega & Joaquin Munoz-Garcia, 2008. "A comparison of classification models to identify the Fragile X Syndrome," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(3), pages 233-244.
- A. Pozdnoukhov & L. Foresti & M. Kanevski, 2009. "Data-driven topo-climatic mapping with machine learning methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(3), pages 497-518, September.
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- Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
- Weizhang Liang & Suizhi Luo & Guoyan Zhao & Hao Wu, 2020. "Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms," Mathematics, MDPI, vol. 8(5), pages 1-17, May.
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- Chongchong Qi & Andy Fourie & Xuhao Du & Xiaolin Tang, 2018. "Prediction of open stope hangingwall stability using random forests," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(2), pages 1179-1197, June.
- Ning Li & Masoud Zare & Congke Yi & Rafael Jimenez, 2022. "Stability Risk Assessment of Underground Rock Pillars Using Logistic Model Trees," IJERPH, MDPI, vol. 19(4), pages 1-19, February.
- Chao Chen & Jian Zhou & Tao Zhou & Weixun Yong, 2021. "Evaluation of vertical shaft stability in underground mines: comparison of three weight methods with uncertainty theory," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(2), pages 1457-1479, November.
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Keywords
Pillar stability; Pillar design; Hard rock mine; Supervised learning; Classification; Repeated cross-validation; R system;All these keywords.
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