Fernández, R;
Correal, JF;
D'Ayala, D;
Medaglia, AL;
(2023)
Towards disaster risk mitigation on large-scale school intervention programs.
International Journal of Disaster Risk Reduction
, 90
, Article 103655. 10.1016/j.ijdrr.2023.103655.
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Abstract
Education infrastructure is one of the main barriers on school quality in Low- and Middle-Income Countries (L&MICs), since it is insufficient and unevenly distributed. Improving the school infrastructure is needed to provide a high-quality education environment. Although research on how to improve the infrastructure is available, there is still a lack of a consistent and systematic approach to develop large-scale interventions at the national or regional level. To fill this gap, we propose a data-driven methodology with the purpose of developing a prioritization of interventions to carry out a seismic disaster risk reduction program. The method starts by identifying groups of similar buildings using clustering analysis, starting with a seismic taxonomy as descriptor (i.e., model input). Then, domain experts analyze the suggested clusters to design scalable interventions for the representative building of each cluster. The proposed data-driven methodology requires experts’ criteria in each step to validate the results and make them applicable, but significantly reduces the bias by automating the decision-making process. We use as case study the Dominican Republic public school infrastructure and present the results of the application of the proposed method. The method presented herein is extensible to other infrastructure portfolios, as well as to other types of hazards.
Type: | Article |
---|---|
Title: | Towards disaster risk mitigation on large-scale school intervention programs |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1016/j.ijdrr.2023.103655 |
Publisher version: | https://doi.org/10.1016/j.ijdrr.2023.103655 |
Language: | English |
Additional information: | © 2023 The Authors. Published by Elsevier Ltd. under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | Disaster mitigation, Disaster preparedness, School infrastructure, Clustering analysis, Unsupervised machine learning, Machine learningEducation |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
URI: | https://discovery.ucl.ac.uk/id/eprint/10168298 |
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