Assessing the Impact of Multi-hazard Events in Spain: A Clustering Index Framework

Matheus Puime Pedra (), Josune Hernantes () and Leire Labaka ()
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Matheus Puime Pedra: TECNUN, University of Navarra
Josune Hernantes: TECNUN, University of Navarra
Leire Labaka: TECNUN, University of Navarra

A chapter in Dynamics of Disasters, 2024, pp 233-251 from Springer

Abstract: Abstract An increasing number of climatic disasters occur nowadays, causing social and financial losses. There is a growing need to understand these events better, how systems cope with these stresses and how to prepare for and mitigate the effects of disasters. The central concept that should be primarily assessed is system resilience. The greater the resilience of a system, the greater its resistance when an event occurs and the lower the losses. Data collection, analysis and means to assess resilience are lacking. Thus, this chapter presents the DRI- Framework, a framework to assess it, through indices, in the face of single climate-induced disasters and a complete view of climate change (CC)-related disasters. This framework utilises data regarding rapidity and robustness as essential objectives to achieve satisfactory resilience. To generate such indexes, machine learning models were used. The framework applicability was tested using a Spanish province scenario. With the obtained results, it was possible to identify the less resilient regions in the face of single disasters and an overview of the resilience considering all CC disasters. DRI-Framework can be essential for stakeholders to comprehend how each system faces an event and be able to transform such analysis into preparation and mitigation actions.

Keywords: Resilience; Climate change; Spain; Machine learning; Clustering; Resilience assessment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-74006-0_10

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DOI: 10.1007/978-3-031-74006-0_10

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