Assessment of tsunami hazards in ports and their impact on marine vessels derived from tsunami models and the observed damage data

Muhari, Abdul; Charvet, Ingrid; Futami, Tsuyoshi; Suppasri, Anawat; Imamura, Fumihiko

doi:10.1007/s11069-015-1772-0

Cited by 34 publications

(32 citation statements)

References 25 publications

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“…After the 2011 tsunami, Suppasri et al (2014) and Muhari et al (2015) developed fragility functions for fishing boats. Based on their results, the threshold water level and flow velocity values for the complete destruction of small boats of less than 5 t are 2 m and 1 m s −1 , respectively.…”

Section: Review Of Previous Studiesmentioning

confidence: 99%

“…The 2011 tsunami was numerically simulated using a set of nonlinear shallow-water equations, which were discretized using the staggered leap-frog finitedifference scheme (TUNAMI model;Imamura, 1996) with bottom friction in the form of Manning's formula, similar to previous studies Charvet et al, 2015;Macabuag et al, 2016). Six computational domains adopted from a previous study which used original data from the Geospatial Information Authority of Japan (GSI, 2015) were used as a nesting grid system of 1215 m (region 1), 405 m (region 2), 135 m (region 3), 45 m (region 4), 15 m (region 5), and 5 m (region 6).…”

Section: Simulation Conditionsmentioning

confidence: 99%

“…Considerable economic damage resulting from the loss of aquaculture products and the impact to ecological systems was also caused by this tsunami. Since the 2004 Indian Ocean tsunami and the 2011 tsunami, numerous quantitative measures of tsunami vulnerability, such as fragility functions, have been developed for buildings (Leelawat et al, 2014;Charvet et al, 2015Charvet et al, , 2017Suppasri et al, 2013, infrastructures (Shoji and Nakamura, 2017), and marine vessels Muhari et al, 2015). However, only one criterion is based on a previous study of the 1960 Chilean tsunami, which struck the west side of Japan: damage to an aquaculture raft (pearl) begins to occur when the tsunami flow velocity is larger than 1 m s −1 regardless of the water level (Nagano et al, 1991).…”

Section: Introductionmentioning

confidence: 99%

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Developing fragility functions for aquaculture rafts and eelgrass in the case of the 2011 Great East Japan tsunami

Suppasri

Fukui

Yamashita

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Since the two devastating tsunamis in 2004 (Indian Ocean) and 2011 (Great East Japan), new findings have emerged on the relationship between tsunami characteristics and damage in terms of fragility functions. Human loss and damage to buildings and infrastructures are the primary target of recovery and reconstruction; thus, such relationships for offshore properties and marine ecosystems remain unclear. To overcome this lack of knowledge, this study used the available data from two possible target areas (Mangokuura Lake and Matsushima Bay) from the 2011 Japan tsunami. This study has three main components: (1) reproduction of the 2011 tsunami, (2) damage investigation, and (3) fragility function development. First, the source models of the 2011 tsunami were verified and adjusted to reproduce the tsunami characteristics in the target areas. Second, the damage ratio (complete damage) of the aquaculture raft and eelgrass was investigated using satellite images taken before and after the 2011 tsunami through visual inspection and binarization. Third, the tsunami fragility functions were developed using the relationship between the simulated tsunami characteristics and the estimated damage ratio. Based on the statistical analysis results, fragility functions were developed for Mangokuura Lake, and the flow velocity was the main contributor to the damage instead of the wave amplitude. For example, the damage ratio above 0.9 was found to be equal to the maximum flow velocities of 1.3 m s −1 (aquaculture raft) and 3.0 m s −1 (eelgrass). This finding is consistent with the previously proposed damage criterion of 1 m s −1 for the aquaculture raft. This study is the first step in the development of damage assessment and planning for marine products and environmental factors to mitigate the effects of future tsunamis.

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Section: Review Of Previous Studiesmentioning

confidence: 99%

Section: Simulation Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Developing fragility functions for aquaculture rafts and eelgrass in the case of the 2011 Great East Japan tsunami

Suppasri

Fukui

Yamashita

et al. 2018

Nat. Hazards Earth Syst. Sci.

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“…• Linear models that utilize linear least squares regression-most commonly applied in fragility studies (Tables 1 and 2), • GLM (e.g., Reese et al, 2011;Charvet et al, 2014a;Muhari et al, 2015;Macabuag et al, 2016a,b;De Risi et al, 2017a,b), • Generalized Additive Models (Macabuag et al, 2016a,b).…”

Section: Model Qualitymentioning

confidence: 99%

Estimating Tsunami-Induced Building Damage through Fragility Functions: Critical Review and Research Needs

Charvet

Macabuag

Rossetto

2017

Front. Built Environ.

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Tsunami damage, fragility, and vulnerability functions are statistical models that provide an estimate of expected damage or losses due to tsunami. They allow for quantification of risk, and so are a vital component of catastrophe models used for human and financial loss estimation, and for land-use and emergency planning. This paper collates and reviews the currently available tsunami fragility functions in order to highlight the current limitations, outline significant advances in this field, make recommendations for model derivation, and propose key areas for further research. Existing functions are first presented, and then key issues are identified in the current literature for each of the model components: building damage data (the response variable of the statistical model), tsunami intensity data (the explanatory variable), and the statistical model that links the two. Finally, recommendations are made regarding areas for future research and current best practices in deriving tsunami fragility functions (see Discussion, Recommendations, and Future Research). The information presented in this paper may be used to assess the quality of current estimations (both based on the quality of the data, and the quality of the models and methods adopted) and to adopt best practice when developing new fragility functions.

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“…The average of the error rates for all iterations gives an estimate of the true prediction error rate [shown in (2)]. Cross validation has been used to estimate tsunami fragility curve prediction error rates by Muhari et al (2015) and Charvet et al (2014a, b), who also propose a penalised error estimation method [shown in (3)] for multinomial models such as those used in this study. In (3), N DS refers to the number of damage states (including DS0, no damage), and the predicted damage state (ds j,predicted ) for the jth observation is taken as the damage state that has the greatest probability of occurrence.…”

Section: It Is Recommended Thatmentioning

confidence: 99%

A proposed methodology for deriving tsunami fragility functions for buildings using optimum intensity measures

et al. 2016

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Tsunami fragility curves are statistical models which form a key component of tsunami risk models, as they provide a probabilistic link between a tsunami intensity measure (TIM) and building damage. Existing studies apply different TIMs (e.g. depth, velocity, force etc.) with conflicting recommendations of which to use. This paper presents a rigorous methodology using advanced statistical methods for the selection of the optimal TIM for fragility function derivation for any given dataset. This methodology is demonstrated using a unique, detailed, disaggregated damage dataset from the 2011 Great East Japan earthquake and tsunami (total 67,125 buildings), identifying the optimum TIM for describing observed damage for the case study locations. This paper first presents the proposed methodology, which is broken into three steps: (1) exploratory analysis, (2) statistical model selection and trend analysis and (3) comparison and selection of TIMs. The case study dataset is then presented, and the methodology is then applied to this dataset. In Step 1, exploratory analysis on the case study dataset suggests that fragility curves should be constructed for the sub-categories of engineered (RC and steel) and nonengineered (wood and masonry) construction materials. It is shown that the exclusion of buildings of unknown construction material (common practice in existing studies) may introduce bias in the results; hence, these buildings are estimated as engineered or nonengineered through use of multiple imputation (MI) techniques. In Step 2, a sensitivity analysis of several statistical methods for fragility curve derivation is conducted in order to select multiple statistical models with which to conduct further exploratory analysis and the TIM comparison (to draw conclusions which are non-model-specific). Methods of data aggregation and ordinary least squares parameter estimation (both used in existing studies) are rejected as they are quantitatively shown to reduce fragility curve accuracy and increase uncertainty. Partially ordered probit models and generalised additive models 123Nat Hazards (2016) 84:1257-1285 DOI 10.1007/s11069-016-2485 (GAMs) are selected for the TIM comparison of Step 3. In Step 3, fragility curves are then constructed for a number of TIMs, obtained from numerical simulation of the tsunami inundation of the 2011 GEJE. These fragility curves are compared using K-fold crossvalidation (KFCV), and it is found that for the case study dataset a force-based measure that considers different flow regimes (indicated by Froude number) proves the most efficient TIM. It is recommended that the methodology proposed in this paper be applied for defining future fragility functions based on optimum TIMs. With the introduction of several concepts novel to the field of fragility assessment (MI, GAMs, KFCV for model optimisation and comparison), this study has significant implications for the future generation of empirical and analytical fragility functions.

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Assessment of tsunami hazards in ports and their impact on marine vessels derived from tsunami models and the observed damage data

Cited by 34 publications

References 25 publications

Developing fragility functions for aquaculture rafts and eelgrass in the case of the 2011 Great East Japan tsunami

Developing fragility functions for aquaculture rafts and eelgrass in the case of the 2011 Great East Japan tsunami

Estimating Tsunami-Induced Building Damage through Fragility Functions: Critical Review and Research Needs

A proposed methodology for deriving tsunami fragility functions for buildings using optimum intensity measures

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