Application of Remote Sensing for Tsunami Disaster

Suppasri, Anawat; Koshimura, Shunichi; Matsuoka, M.; Gokon, Hideomi; Kamthonkiat, Daroonwan

doi:10.5772/32136

Cited by 25 publications

(17 citation statements)

References 14 publications

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“…Then, it is possible to obtain damage Table 2. Summary of statistical parameters for developed fragility curves (modified from Suppasri et al, 2012a). The parameters µ and σ are the mean and standard deviations of the normal distribution, while µ and σ are similar parameters in a standardized lognormal distribution.…”

Section: Discussionmentioning

confidence: 99%

“…Velocity, hydrodynamic force, inundation flow and inundation depth are some of the parameters used by previous research studies to express the tsunami fragility or fatality ratio. Tsunami fragility curves have been developed so far in response to the Indian Ocean tsunami of 2004 (Koshimura and Yanagisawa, 2007;Suppasri et al, 2011;Murao and Nakazato, 2010), the Samoan tsunami of 2009 (Gokon et al, 2011), the historic tsunami caused by the 1993 Hokkaido Nansei-oki earthquake in Okushiri, Japan (Koshimura and Kayaba, 2010;Suppasri et al, 2012a), and recently after the 2011 Great East Japan Earthquake (Suppasri et al, 2012b). However, architecture and engineering of these countries are of different characteristics from the ones applied in the southwestern Pacific coast.…”

Section: Developing Tsunami Fragility Curvesmentioning

confidence: 99%

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Developing Tsunami fragility curves using remote sensing and survey data of the 2010 Chilean Tsunami in Dichato

Mas

Koshimura

Suppasri

et al. 2012

Nat. Hazards Earth Syst. Sci.

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Abstract. On 27 February 2010, a megathrust earthquake of M w = 8.8 generated a destructive tsunami in Chile. It struck not only Chilean coast but propagated all the way to Japan. After the event occurred, the post-tsunami survey team was assembled, funded by the Japan Science and Technology Agency (JST), to survey the area severely affected by the tsunami. The tsunami damaged and destroyed numerous houses, especially in the town of Dichato. In order to estimate the structural fragility against tsunami hazard in this area, tsunami fragility curves were developed. Surveyed data of inundation depth and visual inspection of satellite images of Dichato were used to classify the damage to housing. A practical method suitable when there are limitations on available data for numerical simulation or damage evaluation from surveys is presented here. This study is the first application of tsunami fragility curves on the South American Pacific coast and it might be of practical use for communities with similar characteristics along the west Pacific coast. The proposed curve suggests that structures in Dichato will be severely damaged -with a 68 % probability -already at 2 m tsunami inundation depth.

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Section: Discussionmentioning

confidence: 99%

Section: Developing Tsunami Fragility Curvesmentioning

confidence: 99%

Developing Tsunami fragility curves using remote sensing and survey data of the 2010 Chilean Tsunami in Dichato

Mas

Koshimura

Suppasri

et al. 2012

Nat. Hazards Earth Syst. Sci.

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“…Damage levels are typically defined prior to a post-tsunami survey by engineering teams and describe the condition of the affected structure, from zero damage to complete failure, thus forming a damage scale. Such scale is then used in combination with tsunami flow depth measurements (Ruangrassamee et al 2006;Mas et al 2012;Suppasri et al 2012aSuppasri et al , 2013a or results from numerical simulations (Koshimura et al 2009;Suppasri et al 2011Suppasri et al , 2012b in order to classify the surveyed buildings according to their damage state and a corresponding IM. The first column of Table 1 presents the damage scale defined by the Ministry of Land, Infrastructure, Transport and Tourism in Japan (MLIT) for the survey of the buildings affected by the Great East Japan tsunami that struck the country on March, 11, 2011.…”

Section: Introductionmentioning

confidence: 99%

“…The first column of Table 1 presents the damage scale defined by the Ministry of Land, Infrastructure, Transport and Tourism in Japan (MLIT) for the survey of the buildings affected by the Great East Japan tsunami that struck the country on March, 11, 2011. Fragility functions are derived by applying regression analysis techniques to the classified observations, using damage state as response variable and the chosen IM (most commonly the tsunami flow depth-e.g., Suppasri et al 2013a, b; in some cases numerically estimated flow velocities, or analytically estimated hydrodynamic forces-e.g., Suppasri et al 2012b) as the explanatory variable. Typically, fragility functions are derived by using linear least squares regression, assuming that the response to be modeled follows a normal or lognormal distribution, and by grouping or re-regrouping the data into bins of tsunami intensity (e.g., Suppasri et al 2011Suppasri et al , 2012a Nevertheless, it was shown by Charvet et al (2013b) and Charvet et al (2014a, b) that considerable uncertainty was introduced in the damage predictions when such methods are used as a tool to analyze building damage data.…”

Section: Introductionmentioning

confidence: 99%

A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

et al. 2015

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The recent losses caused by the unprecedented 2011 Great East Japan Tsunami disaster have stimulated further research efforts, notably in the mechanisms and probabilistic determination of tsunami-induced damage, in order to provide the necessary information for future risk assessment and mitigation. The stochastic approach typically adopts fragility functions, which express the probability that a building will reach or exceed a predefined damage level usually for one, sometimes several measures of tsunami intensity. However, improvements in the derivation of fragility functions are still needed in order to yield reliable predictions of tsunami damage to buildings. In particular, extensive disaggregated databases, as well as measures of tsunami intensity beyond the commonly used tsunami flow depth should be used to potentially capture variations in the data which have not been explained by previous models. This study proposes to derive fragility functions with additional intensity measures for the city of Kesennuma, which was extensively damaged during the 2011 tsunami and for which a large and disaggregated dataset of building damage is available. In addition to the surveyed tsunami flow depth, the numerically estimated flow velocities as well as a binary indicator of debris impact are included in the model and used simultaneously to estimate building damage probabilities. Following the recently proposed methodology for fragility estimation based on generalized linear models, which overcomes the shortcomings of classic linear regression in fragility analyses, ordinal regression is applied and the reliability of the model estimates is assessed using a proposed penalized accuracy measure, more suitable than the traditional classification error rate for ordinal models. In order to assess the predictive power of the model, penalized accuracy is estimated through a repeated tenfold cross-validation scheme. For the first time, multivariate tsunami fragility functions are derived and represented in the 123Nat Hazards (2015) 79:2073-2099 DOI 10.1007/s11069-015-1947 form of fragility surfaces. The results show that the model is able to predict tsunami damage with satisfactory predictive accuracy and that debris impact is a crucial factor in the determination of building collapse probabilities.

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“…The 2004 Indian Ocean Tsunami caused devastation in 11 countries, and the monetary damage was approximated around 10 billion US dollars with extensive infrastructure damage [2]. The 2010 Chilean tsunami and earthquake resulted in a few hundred causalities; 81,000 structures were destroyed and around 109,000 were severely damaged [3]. The 2011 Great East Japan tsunami caused property damage of around 300 billion US dollars, with more than 400,000 buildings reported to be damaged or destroyed [4].…”

Section: Introductionmentioning

confidence: 99%

Building Damage Assessment Using Scenario Based Tsunami Numerical Analysis and Fragility Curves

Rehman

Cho

2016

Water

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A combination of a deterministic approach and fragility analysis is applied to assess tsunami damage caused to buildings. The area selected to validate the model is Imwon Port in Korea. The deterministic approach includes numerical modeling of tsunami propagation in the East Sea following an earthquake on the western coast of Japan. The model is based on the linear shallow-water equations (LSWE) augmented with Boussinesq approximation to account for dispersion effects in wave propagation, and coastal wave run-up is modeled by non-linear shallow-water equations (NLSWE). The output from the deterministic model comprises inundation depth. The numerical output is used to perform fragility analysis for buildings vulnerable to flooding by tsunamis in the port area. Recently developed fragility curves-based on the ordinal regression method-are used for damage probability estimates. The extent of structural damage in the areas under a tsunami hazard is identified by the numerical modeling of tsunami features. Our numerical model offers high bathymetric resolution, which enables us to capture flow features at the individual structure level and results in improved estimation of damage probability. This approach can serve as a measure of assessing structure vulnerability for areas with little or no records of tsunami damage and provide planners with a better understanding of structure behavior when a tsunami strikes.

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Application of Remote Sensing for Tsunami Disaster

Cited by 25 publications

References 14 publications

Developing Tsunami fragility curves using remote sensing and survey data of the 2010 Chilean Tsunami in Dichato

Developing Tsunami fragility curves using remote sensing and survey data of the 2010 Chilean Tsunami in Dichato

A multivariate generalized linear tsunami fragility model for Kesennuma City based on maximum flow depths, velocities and debris impact, with evaluation of predictive accuracy

Building Damage Assessment Using Scenario Based Tsunami Numerical Analysis and Fragility Curves

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