Seismic vulnerability and risk assessment using multimodal data and machine learning: a case study of the central urban area of Jinan City, China

Abstract

Seismic hazards pose a major threat to life safety, social development, and the economy. Traditional seismic vulnerability and risk assessments, such as field survey methods, may not be suitable for densely built-up urban areas due to the limited availability of comprehensive data and potential subjectivity in judgment. To overcome these limitations, an integrated method for seismic vulnerability and risk assessment based on multimodal remote sensing data, support vector machine (SVM) and GIScience methods was proposed and applied to the central urban area of Jinan City, Shandong Province, China. First, an area with representative buildings was selected for field survey research, and an attribute information base established. Then, the SVM method was used to establish the susceptibility proxies, which were applied to the whole study area after accuracy evaluation. Finally, the spatial distribution of seismic vulnerability and risk under different seismic intensity scenarios (from VI to X) was analyzed in GIScience. The results show that the average building vulnerability index in the central urban area of Jinan City is 0.53, indicating that the overall seismic performance of buildings is at a moderate level. Under the seismic intensity scenario of VIII, the buildings in the Starting area and New urban district of Jinan would mostly suffer ‘Moderate’ damage, while Old urban areas, with more seismic-resistant buildings, would experience only ‘Slight’ damage. This study aims to offer an efficient and accurate method for assessing seismic vulnerability in mid to large-sized cities characterized by concentrated population densities and rapid urbanization, as well as provide a valuable reference for efforts in urban renewal, seismic mitigation, and land planning, particularly in cities and regions of developing countries. Additionally, it contributes to the realization of Sustainable Development Goal 11, which seeks to make cities and human settlements inclusive, safe, resilient, and sustainable.

Graphical abstract

Keywords

seismic vulnerability assessment / GIScience / EMS-98 / SVM / RISK-UE / multimodal remote sensing data

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