Seismic vulnerability and risk assessment at the urban scale using support vector machine and GIScience technology: a case study of the Lixia District in Jinan City, China

• Published in: Geomatics, natural hazards and risk Vol. 14; no. 1
• Main Authors: Liu, Yaohui, Zhang, Xinyu, Liu, Wenyi, Lin, Yu, Su, Fei, Cui, Jian, Wei, Benyong, Cheng, Hao, Gross, Lutz
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 31.12.2023; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Climate action; Disasters; Earthquake damage; Emergency plans; EMS-98; Environmental risk; Field investigations; Geological hazards; GIScience; International organizations; Population density; Remote sensing; Risk assessment; RISK-UE; Seismic activity; Seismic hazard; Seismic response; Seismic surveys; Seismic vulnerability; support vector machine; Support vector machines; Sustainable development; Sustainable Development Goals; urban scale; Vulnerability; China
• ISSN: 1947-5705; 1947-5713
• DOI: 10.1080/19475705.2023.2173663

The increase in the number and severity of seismic disasters has put communities in danger, especially in rapidly developing and densely populated areas. Traditional seismic vulnerability and risk assessment methods, including field investigation, are accurate at the building scale; however, their low-efficiency and high-cost characteristics limit the application of these methods in urban-scale regions with high-speed development and risk exposure. To address this issue, multisource remote sensing interpretation, support vector machine (SVM) and geographic information science (GIScience) technologies are combined to test the performance and efficiency of a urban-scale macroscopic seismic vulnerability and risk assessment method in the Lixia District of Jinan City, Shandong Province, China, which is characterized by rapid development, a variety of building types, and moderate-to-low seismic risk. First, a traditional field survey was conducted in Lixia District, and a building attribute information database was constructed. Second, the vulnerability proxies of building attribute information and building seismic vulnerability were estimated based on the EMS-98 standard and the SVM. Finally, vulnerability proxies established based on the RISK-UE model were applied to the Lixia database, and the vulnerability and risk assessment under different seismic intensities were estimated with the experimental accuracy verified. The results showed that the SVM method can obtain stable and accurate results in urban scale vulnerability assessment. The mean building vulnerability index in Lixia District is 0.43, which indicates that the overall seismic performance is good. Most of the area falls within the seismic intensity range of VII-X degrees and would experience slight to moderate damage. The results of the study contribute to enhancing the precision and efficacy of large-scale seismic risk assessment, and they can be used by relevant departments to create tailored emergency plans and reduce seismic hazard losses. Additionally, these results can aid in achieving the climate action goal (SDG13) of the United Nations Sustainable Development Goals (SDGs).