Tsunami inundation area and run-up height in the Iwate coastal region following the Great East Japan Earthquake as estimated from aerial photographs and digital elevation data

Yanagawa, Ryoichi; Koshino, Shuzo

doi:10.1007/s11069-016-2285-1

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…Even the numerical simulation is unable to approximate extremely high inundation points of more than 25 m located outside the bay (marked in Figure 7a). These extreme run‐ups were possibly induced by the steep cliff and were also observed at the shorelines along the Sanriku coasts characterized by similar terrain (Yanagawa & Koshino, 2016). Consequently, the performance of the numerical simulation in Otsuchi ( K = 1.144 and κ = 1.257) (Figure 7a) is inferior compared to Rikuzentakata.…”

Section: Resultsmentioning

confidence: 82%

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Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

2020

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We explore recent developments in computer science on deep learning to estimate high-resolution tsunami inundation from a quick low-resolution computation result. Deep network architecture is capable of storing large information acquired via a training/learning process by pairing low-and high-resolution deterministic simulation results from precalculated hypothetical scenarios. In a real case, with a real-time source estimate and linear simulation computed on a relatively low-grid resolution, optimized network parameters can be used to rapidly and accurately transform low-resolution simulation outputs to higher-resolution grids. We generate 532 source scenarios of interplate earthquakes (Mw 8-9) along the Japan Trench subduction zone to simulate tsunamis and utilize a precalculated library for deep learning. To test the proposed method, we consider a realistic source model inferred from static ground displacements and offshore tsunami data presumably available in real-time and compare forecasted inundation heights against observations in Rikuzentakata and Otsuchi cities associated with the 2011 Tohoku-oki tsunami. Our results show that the proposed method exhibits comparable accuracy to the conventional physics-based simulation but achieves approximately 90% reduction of real-time computational efforts. Thus, it has good potential as a future tsunami forecasting algorithm.

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Section: Resultsmentioning

confidence: 82%

“…terrain (Yanagawa & Koshino, 2016). Consequently, the performance of the numerical simulation in Otsuchi (K ¼ 1.144 and κ ¼ 1.257) ( Figure 7a) is inferior compared to Rikuzentakata.…”

Section: 1029/2020jb019690mentioning

confidence: 96%

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

2020

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“…However, this is not always the case because of the high degree of nonlinearity of inundation flow depths, particularly over complex regions. Higher misfits are apparent near shorelines with complex coastal geometry and steep cliff typically characterizing the southern Sanriku coasts 29 (Fig. 5b ).…”

Section: Resultsmentioning

confidence: 99%

Machine learning-based tsunami inundation prediction derived from offshore observations

et al. 2022

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The world’s largest and densest tsunami observing system gives us the leverage to develop a method for a real-time tsunami inundation prediction based on machine learning. Our method utilizes 150 offshore stations encompassing the Japan Trench to simultaneously predict tsunami inundation at seven coastal cities stretching ~100 km along the southern Sanriku coast. We trained the model using 3093 hypothetical tsunami scenarios from the megathrust (Mw 8.0–9.1) and nearby outer-rise (Mw 7.0–8.7) earthquakes. Then, the model was tested against 480 unseen scenarios and three near-field historical tsunami events. The proposed machine learning-based model can achieve comparable accuracy to the physics-based model with ~99% computational cost reduction, thus facilitates a rapid prediction and an efficient uncertainty quantification. Additionally, the direct use of offshore observations can increase the forecast lead time and eliminate the uncertainties typically associated with a tsunami source estimate required by the conventional modeling approach.

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“…The tsunami caused by the GEJE also had significant impacts. In Iwate Prefecture, a maximum run-up height of 39.71 m was directly observed during the 2011 tsunami (Mori et al, 2012), whereas results from geographic information system (GIS) analyses estimated a maximum run-up height of 49.8 ± 0.2 m (Yanagawa et al, 2016). On March 11, 2014, Japan's National Police Agency confirmed 18,517 dead and missing persons within 12 prefectural and city government jurisdictions as a result of the tsunami caused by the GEJE and its aftershocks.…”

Section: Introductionmentioning

confidence: 99%

“…Each of the other areas had significantly higher Hatori destruction ratios. Among them, 15 areas had ratios over 90%, indicating that most or all of the buildings were washed away or completely destroyed (Yanagawa et al, 2014b).…”

Section: Introductionmentioning

confidence: 99%

Development and Application of a Building Group Destruction Probability Model Based on the Great East Japan Earthquake Tsunami Experience

Yanagawa

2017

Journal of Natural Disaster Science

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This study examined the regional spatial characteristics of tsunami flooding and building damage using geographic information systems. An analytical model that evaluates total building destruction risk was developed using building damage data from coastal areas in Iwate Prefecture. Building density characteristics in the study area were categorized into four types of environments: (1) many isolated buildings, (2) combination of isolated and neighboring buildings, (3) combination of neighboring and surrounding buildings, and (4) many neighboring buildings. Many isolated buildings were located along the narrow, low-lying areas facing the Pacific Ocean. In comparison, higher building densities were observed along the inner part of the enclosed gulf topography. Most buildings located near the shoreline collapsed. Farther inland, a higher percentage of buildings experienced half-collapse or no-damage. Closer inspection of the varying spatial distribution characteristics and resultant building damage among the 27 target areas led to the identification of several key indicators for predicted building damage including the structure, use, and density of the affected building and the extent of tsunami inundation. Based on the building damage characteristics following the 2011 tsunami, a building group destruction probability model was developed and verified. The proposed model successfully estimated building collapse ratios using the available data.

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Tsunami inundation area and run-up height in the Iwate coastal region following the Great East Japan Earthquake as estimated from aerial photographs and digital elevation data

Cited by 3 publications

References 3 publications

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

Applying a Deep Learning Algorithm to Tsunami Inundation Database of Megathrust Earthquakes

Machine learning-based tsunami inundation prediction derived from offshore observations

Development and Application of a Building Group Destruction Probability Model Based on the Great East Japan Earthquake Tsunami Experience

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