The prediction model of earthquake casuailty based on robust wavelet v-SVM
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DOI: 10.1007/s11069-015-1620-2
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Cited by:
- Xing Huang & Mengjie Luo & Huidong Jin, 2020. "Application of improved ELM algorithm in the prediction of earthquake casualties," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-13, June.
- Huang Xing & Song Junyi & Huidong Jin, 2020. "The casualty prediction of earthquake disaster based on Extreme Learning Machine method," 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. 102(3), pages 873-886, July.
- Camila Pareja Yale & Hugo Tsugunobu Yoshida Yoshizaki & Luiz Paulo Fávero, 2022. "A New Zero-Inflated Negative Binomial Multilevel Model for Forecasting the Demand of Disaster Relief Supplies in the State of Sao Paulo, Brazil," Mathematics, MDPI, vol. 10(22), pages 1-11, November.
- Fei, Liguo & Wang, Yanqing, 2022. "Demand prediction of emergency materials using case-based reasoning extended by the Dempster-Shafer theory," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
- Muhammet Gul & Ali Fuat Guneri, 2016. "An artificial neural network-based earthquake casualty estimation model for Istanbul city," 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. 84(3), pages 2163-2178, December.
- Chen, Weiyi & Zhang, Limao, 2022. "An automated machine learning approach for earthquake casualty rate and economic loss prediction," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
- Shao, Jianfang & Liang, Changyong & Liu, Yujia & Xu, Jian & Zhao, Shuping, 2021. "Relief demand forecasting based on intuitionistic fuzzy case-based reasoning," Socio-Economic Planning Sciences, Elsevier, vol. 74(C).
- Manhao Luo & Shuangyun Peng & Yanbo Cao & Jing Liu & Bangmei Huang, 2023. "Earthquake fatality prediction based on hybrid feature importance assessment: a case study in Yunnan Province, China," 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. 116(3), pages 3353-3376, April.
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Keywords
Earthquake casualty prediction; Model construction; RW v-SVM; Loss function;All these keywords.
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