TY - GEN
T1 - Advances of tsunami inundation forecasting and its future perspectives
AU - Koshimura, Shunichi
AU - Hino, Ryota
AU - Ohta, Yusaku
AU - Kobayashi, Hiroaki
AU - Murashima, Yoichi
AU - Musa, Akihiro
N1 - Funding Information:
The research was partly funded by the CREST project of the Japan Science and Technology Agency (JST) and the Grantsin-Aid for Scientific Research of MEXT/JSPS (Grant number 25242035).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/25
Y1 - 2017/10/25
N2 - Bringing together state-of-the-art high-performance computing, remote sensing and spatial information sciences, we established a real-time tsunami inundation forecasting, damage estimation and mapping system to enhance disaster response. Using Tohoku University's vector supercomputer SX-ACE, we accomplished '10-10-10 challenge', to complete tsunami source determination in 10 minutes, tsunami inundation modeling and mapping impacts in 10 minutes with 10 m grid resolution. Given the maximum flow depth distribution, we perform quantitative estimation of exposed population using census data, and the numbers of damaged structures by applying tsunami fragility curves. After the potential tsunami-affected areas and structural damage are estimated, the analysis gets focused and moves on to the 'verification' phase using remote sensing. A semi-automated method to detect building damage in the tsunami-affected areas is developed using high-resolution SAR (Synthetic Aperture Radar) data so that the simulated results are verified and obtain a feedback to improve the accuracy of tsunami inundation forecasting.
AB - Bringing together state-of-the-art high-performance computing, remote sensing and spatial information sciences, we established a real-time tsunami inundation forecasting, damage estimation and mapping system to enhance disaster response. Using Tohoku University's vector supercomputer SX-ACE, we accomplished '10-10-10 challenge', to complete tsunami source determination in 10 minutes, tsunami inundation modeling and mapping impacts in 10 minutes with 10 m grid resolution. Given the maximum flow depth distribution, we perform quantitative estimation of exposed population using census data, and the numbers of damaged structures by applying tsunami fragility curves. After the potential tsunami-affected areas and structural damage are estimated, the analysis gets focused and moves on to the 'verification' phase using remote sensing. A semi-automated method to detect building damage in the tsunami-affected areas is developed using high-resolution SAR (Synthetic Aperture Radar) data so that the simulated results are verified and obtain a feedback to improve the accuracy of tsunami inundation forecasting.
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U2 - 10.1109/OCEANSE.2017.8084753
DO - 10.1109/OCEANSE.2017.8084753
M3 - Conference contribution
AN - SCOPUS:85044755338
T3 - OCEANS 2017 - Aberdeen
SP - 1
EP - 4
BT - OCEANS 2017 - Aberdeen
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - OCEANS 2017 - Aberdeen
Y2 - 19 June 2017 through 22 June 2017
ER -