Cargando…
Vulnerability indicators for natural hazards: an innovative selection and weighting approach
To prepare for upcoming extreme events, decision makers, scientists and other stakeholders require a thorough understanding of the vulnerability of the built environment to natural hazards. A vulnerability index based on building characteristics (indicators) rather than empirical data may be an alte...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803695/ https://www.ncbi.nlm.nih.gov/pubmed/31636308 http://dx.doi.org/10.1038/s41598-019-50257-2 |
_version_ | 1783460999974617088 |
---|---|
author | Papathoma-Köhle, Maria Schlögl, Matthias Fuchs, Sven |
author_facet | Papathoma-Köhle, Maria Schlögl, Matthias Fuchs, Sven |
author_sort | Papathoma-Köhle, Maria |
collection | PubMed |
description | To prepare for upcoming extreme events, decision makers, scientists and other stakeholders require a thorough understanding of the vulnerability of the built environment to natural hazards. A vulnerability index based on building characteristics (indicators) rather than empirical data may be an alternative approach to a comprehensive physical vulnerability assessment of the building stock. The present paper focuses on the making of such an index for dynamic flooding in mountain areas demonstrating the transferability of vulnerability assessment approaches between hazard types, reducing the amount of required data and offering a tool that can be used in areas were empirical data are not available. We use data from systematically documented torrential events in the European Alps to select and weight the important indicators using an all-relevant feature selection algorithm based on random forests. The permutation-based feature selection reduced the initial number of indicators from 22 to seven, decreasing in this way the amount of required data for assessing physical vulnerability and ensuring that only relevant indicators are considered. The new Physical Vulnerability Index (PVI) may be used in the mountain areas of Europe and beyond where only few empirical data are available supporting decision-making in reducing risk to dynamic flooding. |
format | Online Article Text |
id | pubmed-6803695 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-68036952019-10-24 Vulnerability indicators for natural hazards: an innovative selection and weighting approach Papathoma-Köhle, Maria Schlögl, Matthias Fuchs, Sven Sci Rep Article To prepare for upcoming extreme events, decision makers, scientists and other stakeholders require a thorough understanding of the vulnerability of the built environment to natural hazards. A vulnerability index based on building characteristics (indicators) rather than empirical data may be an alternative approach to a comprehensive physical vulnerability assessment of the building stock. The present paper focuses on the making of such an index for dynamic flooding in mountain areas demonstrating the transferability of vulnerability assessment approaches between hazard types, reducing the amount of required data and offering a tool that can be used in areas were empirical data are not available. We use data from systematically documented torrential events in the European Alps to select and weight the important indicators using an all-relevant feature selection algorithm based on random forests. The permutation-based feature selection reduced the initial number of indicators from 22 to seven, decreasing in this way the amount of required data for assessing physical vulnerability and ensuring that only relevant indicators are considered. The new Physical Vulnerability Index (PVI) may be used in the mountain areas of Europe and beyond where only few empirical data are available supporting decision-making in reducing risk to dynamic flooding. Nature Publishing Group UK 2019-10-21 /pmc/articles/PMC6803695/ /pubmed/31636308 http://dx.doi.org/10.1038/s41598-019-50257-2 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Papathoma-Köhle, Maria Schlögl, Matthias Fuchs, Sven Vulnerability indicators for natural hazards: an innovative selection and weighting approach |
title | Vulnerability indicators for natural hazards: an innovative selection and weighting approach |
title_full | Vulnerability indicators for natural hazards: an innovative selection and weighting approach |
title_fullStr | Vulnerability indicators for natural hazards: an innovative selection and weighting approach |
title_full_unstemmed | Vulnerability indicators for natural hazards: an innovative selection and weighting approach |
title_short | Vulnerability indicators for natural hazards: an innovative selection and weighting approach |
title_sort | vulnerability indicators for natural hazards: an innovative selection and weighting approach |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803695/ https://www.ncbi.nlm.nih.gov/pubmed/31636308 http://dx.doi.org/10.1038/s41598-019-50257-2 |
work_keys_str_mv | AT papathomakohlemaria vulnerabilityindicatorsfornaturalhazardsaninnovativeselectionandweightingapproach AT schloglmatthias vulnerabilityindicatorsfornaturalhazardsaninnovativeselectionandweightingapproach AT fuchssven vulnerabilityindicatorsfornaturalhazardsaninnovativeselectionandweightingapproach |