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Bayesian tsunami fragility modeling considering input data uncertainty
Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, a...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979541/ https://www.ncbi.nlm.nih.gov/pubmed/32025200 http://dx.doi.org/10.1007/s00477-016-1230-x |
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author | De Risi, Raffaele Goda, Katsuichiro Mori, Nobuhito Yasuda, Tomohiro |
author_facet | De Risi, Raffaele Goda, Katsuichiro Mori, Nobuhito Yasuda, Tomohiro |
author_sort | De Risi, Raffaele |
collection | PubMed |
description | Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty. |
format | Online Article Text |
id | pubmed-6979541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-69795412020-02-03 Bayesian tsunami fragility modeling considering input data uncertainty De Risi, Raffaele Goda, Katsuichiro Mori, Nobuhito Yasuda, Tomohiro Stoch Environ Res Risk Assess Original Paper Empirical tsunami fragility curves are developed based on a Bayesian framework by accounting for uncertainty of input tsunami hazard data in a systematic and comprehensive manner. Three fragility modeling approaches, i.e. lognormal method, binomial logistic method, and multinomial logistic method, are considered, and are applied to extensive tsunami damage data for the 2011 Tohoku earthquake. A unique aspect of this study is that uncertainty of tsunami inundation data (i.e. input hazard data in fragility modeling) is quantified by comparing two tsunami inundation/run-up datasets (one by the Ministry of Land, Infrastructure, and Transportation of the Japanese Government and the other by the Tohoku Tsunami Joint Survey group) and is then propagated through Bayesian statistical methods to assess the effects on the tsunami fragility models. The systematic implementation of the data and methods facilitates the quantitative comparison of tsunami fragility models under different assumptions. Such comparison shows that the binomial logistic method with un-binned data is preferred among the considered models; nevertheless, further investigations related to multinomial logistic regression with un-binned data are required. Finally, the developed tsunami fragility functions are integrated with building damage-loss models to investigate the influences of different tsunami fragility curves on tsunami loss estimation. Numerical results indicate that the uncertainty of input tsunami data is not negligible (coefficient of variation of 0.25) and that neglecting the input data uncertainty leads to overestimation of the model uncertainty. Springer Berlin Heidelberg 2016-02-18 2017 /pmc/articles/PMC6979541/ /pubmed/32025200 http://dx.doi.org/10.1007/s00477-016-1230-x Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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. |
spellingShingle | Original Paper De Risi, Raffaele Goda, Katsuichiro Mori, Nobuhito Yasuda, Tomohiro Bayesian tsunami fragility modeling considering input data uncertainty |
title | Bayesian tsunami fragility modeling considering input data uncertainty |
title_full | Bayesian tsunami fragility modeling considering input data uncertainty |
title_fullStr | Bayesian tsunami fragility modeling considering input data uncertainty |
title_full_unstemmed | Bayesian tsunami fragility modeling considering input data uncertainty |
title_short | Bayesian tsunami fragility modeling considering input data uncertainty |
title_sort | bayesian tsunami fragility modeling considering input data uncertainty |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6979541/ https://www.ncbi.nlm.nih.gov/pubmed/32025200 http://dx.doi.org/10.1007/s00477-016-1230-x |
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