Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods
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DOI: 10.1007/s11069-014-1538-0
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Cited by:
- Jian Ma & Anirudh Rao & Vitor Silva & Kai Liu & Ming Wang, 2021. "A township-level exposure model of residential buildings for mainland 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. 108(1), pages 389-423, August.
- L. Gerardo F. Salazar & Tiago Miguel Ferreira, 2020. "Seismic Vulnerability Assessment of Historic Constructions in the Downtown of Mexico City," Sustainability, MDPI, vol. 12(3), pages 1-21, February.
- Zemin Gao & Mingtao Ding, 2022. "Application of convolutional neural network fused with machine learning modeling framework for geospatial comparative analysis of landslide susceptibility," 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. 113(2), pages 833-858, September.
- Eliana Fischer & Giovanni Barreca & Annalisa Greco & Francesco Martinico & Alessandro Pluchino & Andrea Rapisarda, 2023. "Seismic risk assessment of a large metropolitan area by means of simulated earthquakes," 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. 118(1), pages 117-153, August.
- Liqiang An & Jingfa Zhang, 2022. "Impact of Urbanization on Seismic Risk: A Study Based on Remote Sensing Data," Sustainability, MDPI, vol. 14(10), pages 1-25, May.
- 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.
- Jihye Han & Soyoung Park & Seongheon Kim & Sanghun Son & Seonghyeok Lee & Jinsoo Kim, 2019. "Performance of Logistic Regression and Support Vector Machines for Seismic Vulnerability Assessment and Mapping: A Case Study of the 12 September 2016 ML5.8 Gyeongju Earthquake, South Korea," Sustainability, MDPI, vol. 11(24), pages 1-19, December.
- Abdelheq Guettiche & Philippe Guéguen & Mostefa Mimoune, 2017. "Seismic vulnerability assessment using association rule learning: application to the city of Constantine, Algeria," 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. 86(3), pages 1223-1245, April.
- Vera Wendler-Bosco & Charles Nicholson, 2022. "Modeling the economic impact of incoming tropical cyclones using machine learning," 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. 110(1), pages 487-518, January.
- Jihye Han & Jinsoo Kim & Soyoung Park & Sanghun Son & Minji Ryu, 2020. "Seismic Vulnerability Assessment and Mapping of Gyeongju, South Korea Using Frequency Ratio, Decision Tree, and Random Forest," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
- Ismaël Riedel & Philippe Guéguen, 2018. "Modeling of damage-related earthquake losses in a moderate seismic-prone country and cost–benefit evaluation of retrofit investments: application to France," 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. 90(2), pages 639-662, January.
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Keywords
Seismic vulnerability; Moderate hazard; Existing building; Data mining; Support vector machine; Europe;All these keywords.
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