Publication:
Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami

dc.contributor.author Mas E. es_PE
dc.contributor.author Paulik R. es_PE
dc.contributor.author Pakoksung K. es_PE
dc.contributor.author Adriano B. es_PE
dc.contributor.author Moya L. es_PE
dc.contributor.author Suppasri A. es_PE
dc.contributor.author Muhari A. es_PE
dc.contributor.author Khomarudin R. es_PE
dc.contributor.author Yokoya N. es_PE
dc.contributor.author Matsuoka M. es_PE
dc.contributor.author Koshimura S. es_PE
dc.date.accessioned 2024-05-30T23:13:38Z
dc.date.available 2024-05-30T23:13:38Z
dc.date.issued 2020
dc.description.abstract We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific tsunami intensity measure, in this case obtained from the interpolation of multiple surveyed points of tsunami flow depth. We observed that the FS approach led to a more consistent function than that of the VI and MLRS methods. In particular, an initial damage probability observed at zero inundation depth in the latter two methods revealed the effects of misclassifications on tsunami fragility functions derived from VI data; however, it also highlighted the remarkable advantages of MLRS methods. The reasons and insights used to overcome such limitations are discussed together with the pros and cons of each method. The results show that the tsunami damage observed in the 2018 Sulawesi event in Indonesia, expressed in the fragility function developed herein, is similar in shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, albeit with a slightly lower damage probability between zero-to-five-meter inundation depths. On the other hand, in comparison with the fragility function developed after the 2004 Indian Ocean tsunami in Banda Aceh, the characteristics of Palu structures exhibit higher fragility in response to tsunamis. The two-meter inundation depth exhibited nearly 20% probability of damage in the case of Banda Aceh, while the probability of damage was close to 70% at the same depth in Palu. © 2020, The Author(s).
dc.description.sponsorship Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - Concytec
dc.identifier.doi https://doi.org/10.1007/s00024-020-02501-4
dc.identifier.scopus 2-s2.0-85085939746
dc.identifier.uri https://hdl.handle.net/20.500.12390/2545
dc.language.iso eng
dc.publisher Birkhauser
dc.relation.ispartof Pure and Applied Geophysics
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject tsunami
dc.subject 2018 Sulawesi es_PE
dc.subject earthquake es_PE
dc.subject Fragility function es_PE
dc.subject.ocde http://purl.org/pe-repo/ocde/ford#1.05.11
dc.title Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami
dc.type info:eu-repo/semantics/article
dspace.entity.type Publication
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