Estimation of earthquake damage to urban environments using sparse modeling

Yoshiki Ogawa, Yoshihide Sekimoto and Ryosuke Shibasaki

Environment and Planning B, 2021, vol. 48, issue 5, 1075-1090

Abstract: For the establishment of precise disaster prevention measures in response to the Nankai megathrust earthquakes predicted to occur in the future, it is necessary to conduct numerous earthquake simulations and evaluate the vulnerability of the urban environment quantitatively. This vulnerability is evaluated on the basis of factors such as the extent of damage from earthquakes, as well as the attributes of residents, urban infrastructure, and systems in the environment. In this study, we propose a sparse modeling (SpM)-based technique for the evaluation of potential damage to urban environments due to Nankai megathrust earthquakes in Japan. As explanatory variables, any variables related to urban environments in Kochi Prefecture are considered. The results show that, unlike the so-called “complex disaster†events, the number of critical variables that characterize damage states when external disaster forces data (e.g. estimated seismic motion and tsunami height) and urban environment data are available is low, regardless of the magnitude of damage. In other words, urban system variables selected for damage states may be extracted as variables indicating vulnerability to earthquake damage. In addition, we evaluated the characteristics of different cities by visualizing the SpM results on a radar chart. The proposed technique is useful for gaining a deeper understanding of the influence of urban environment variables on earthquake damages. Furthermore, it is expected that measures for improving urban system resilience will be explored based on the proposed technique.

Keywords: Big data; GIS; integrated damage estimation; resilience; sparse modeling; variable selection (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:48:y:2021:i:5:p:1075-1090

DOI: 10.1177/2399808320986560

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