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Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine methods

Author

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  • Ismaël Riedel
  • Philippe Guéguen
  • Mauro Dalla Mura
  • Erwan Pathier
  • Thomas Leduc
  • Jocelyn Chanussot

Abstract

The estimation of the seismic vulnerability of buildings at an urban scale, a crucial element in any risk assessment, is an expensive, time-consuming, and complicated task, especially in moderate-to-low seismic hazard regions, where the mobilization of resources for the seismic evaluation is reduced, even if the hazard is not negligible. In this paper, we propose a way to perform a quick estimation using convenient, reliable building data that are readily available regionally instead of the information usually required by traditional methods. Using a dataset of existing buildings in Grenoble (France) with an EMS98 vulnerability classification and by means of two different data mining techniques—association rule learning and support vector machine—we developed seismic vulnerability proxies. These were applied to the whole France using basic information from national databases (census information) and data derived from the processing of satellite images and aerial photographs to produce a nationwide vulnerability map. This macroscale method to assess vulnerability is easily applicable in case of a paucity of information regarding the structural characteristics and constructional details of the building stock. The approach was validated with data acquired for the city of Nice, by comparison with the RiskUE method. Finally, damage estimations were compared with historic earthquakes that caused moderate-to-strong damage in France. We show that due to the evolution of vulnerability in cities, the number of seriously damaged buildings can be expected to double or triple if these historic earthquakes were to occur today. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Ismaël Riedel & Philippe Guéguen & Mauro Dalla Mura & Erwan Pathier & Thomas Leduc & Jocelyn Chanussot, 2015. "Seismic vulnerability assessment of urban environments in moderate-to-low seismic hazard regions using association rule learning and support vector machine 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. 76(2), pages 1111-1141, March.
  • Handle: RePEc:spr:nathaz:v:76:y:2015:i:2:p:1111-1141
    DOI: 10.1007/s11069-014-1538-0
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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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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