Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1267-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-1267-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Rapid tsunami force prediction by mode-decomposition-based surrogate modeling
Kenta Tozato
Department of Civil and Environmental Engineering, Tohoku University, Aza-Aoba 6-6-06, Aramaki, Aoba-ku, Sendai 980-8579, Japan
Shinsuke Takase
Department of Civil Engineering and Architecture, Hachinohe Institute of Technology, 88-1 Ohbiraki, Myo, Hachinohe, Aomori 031-8501, Japan
Shuji Moriguchi
CORRESPONDING AUTHOR
International Research Institute of Disaster Science, Tohoku University, Aza-Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan
Kenjiro Terada
International Research Institute of Disaster Science, Tohoku University, Aza-Aoba 468-1, Aramaki, Aoba-ku, Sendai 980-8572, Japan
Yu Otake
Department of Civil and Environmental Engineering, Tohoku University, Aza-Aoba 6-6-06, Aramaki, Aoba-ku, Sendai 980-8579, Japan
Yo Fukutani
College of Science and Engineering, Kanto Gakuin University, Mutsuura Higashi 1-50-1, Kanazawa-ku, Yokohama-shi, Kanagawa 236-8501, Japan
Kazuya Nojima
Research and Development Center, Nippon Koei Co., Ltd., Inarihara 2304, Tsukuba-shi, Ibaraki 300-1259, Japan
Masaaki Sakuraba
Business Strategy Headquarters, Digital Innovation Division, Nippon Koei Co., Ltd., 5-4 Kojimachi, Chiyoda-ku, Tokyo 102-8539, Japan
Hiromu Yokosu
Nuclear Safety Research & Development Center, Chubu Electric Power Co., Inc., Sakura 5561, Omaezaki, Shizuoka 437-1695, Japan
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Cited
10 citations as recorded by crossref.
- Optimal probabilistic placement of facilities using a surrogate model for 3D tsunami simulations K. Tozato et al. 10.5194/nhess-23-1891-2023
- Efficient probabilistic prediction of tsunami inundation considering random tsunami sources and the failure probability of seawalls Y. Fukutani et al. 10.1007/s00477-023-02379-3
- REAL-TIME RECONSTRUCTION SIMULATION BASED ON AUTONOMOUS BASIS FUNCTION SELECTION Y. OTAKE et al. 10.2208/jscejj.22-15013
- A neural network-based surrogate model for efficient probabilistic tsunami inundation assessment Y. Fukutani & M. Motoki 10.1016/j.coastaleng.2025.104767
- Surrogate modeling for transient electrochemical potential analysis for SOFC using proper orthogonal decomposition M. Sato et al. 10.1016/j.ssi.2024.116642
- Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates N. Ragu Ramalingam et al. 10.5194/nhess-25-1655-2025
- EFFICIENCY IMPROVEMENT OF PINNS INVERSE ANALYSIS BY EXTRACTING SPATIAL FEATURES OF DATA S. DEGUCHI et al. 10.2208/jscejj.22-15011
- Influence of seismological factors to earthquake-induced tsunami and sensitivity of structural response to orientations M. Naskar et al. 10.1007/s10950-024-10249-w
- Machine learning emulation of high resolution inundation maps E. Briseid Storrøsten et al. 10.1093/gji/ggae151
- Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences R. Nomura et al. 10.1029/2021JC018324
9 citations as recorded by crossref.
- Optimal probabilistic placement of facilities using a surrogate model for 3D tsunami simulations K. Tozato et al. 10.5194/nhess-23-1891-2023
- Efficient probabilistic prediction of tsunami inundation considering random tsunami sources and the failure probability of seawalls Y. Fukutani et al. 10.1007/s00477-023-02379-3
- REAL-TIME RECONSTRUCTION SIMULATION BASED ON AUTONOMOUS BASIS FUNCTION SELECTION Y. OTAKE et al. 10.2208/jscejj.22-15013
- A neural network-based surrogate model for efficient probabilistic tsunami inundation assessment Y. Fukutani & M. Motoki 10.1016/j.coastaleng.2025.104767
- Surrogate modeling for transient electrochemical potential analysis for SOFC using proper orthogonal decomposition M. Sato et al. 10.1016/j.ssi.2024.116642
- Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates N. Ragu Ramalingam et al. 10.5194/nhess-25-1655-2025
- EFFICIENCY IMPROVEMENT OF PINNS INVERSE ANALYSIS BY EXTRACTING SPATIAL FEATURES OF DATA S. DEGUCHI et al. 10.2208/jscejj.22-15011
- Influence of seismological factors to earthquake-induced tsunami and sensitivity of structural response to orientations M. Naskar et al. 10.1007/s10950-024-10249-w
- Machine learning emulation of high resolution inundation maps E. Briseid Storrøsten et al. 10.1093/gji/ggae151
1 citations as recorded by crossref.
Latest update: 31 May 2025
Short summary
This study presents a novel framework for rapid tsunami force predictions through the application of mode-decomposition-based surrogate modeling with 2D–3D coupled numerical simulations. A numerical example is presented to demonstrate the applicability of the proposed framework to one of the tsunami-affected areas during the Great East Japan Earthquake of 2011.
This study presents a novel framework for rapid tsunami force predictions through the...
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