Rapid tsunami force prediction by mode-decomposition-based  surrogate modeling

Tozato, Kenta; Takase, Shinsuke; Moriguchi, Shuji; Terada, Kenjiro; Otake, Yu; Fukutani, Yo; Nojima, Kazuya; Sakuraba, Masaaki; Yokosu, Hiromu

doi:https://doi.org/10.5194/nhess-22-1267-2022

Articles | Volume 22, issue 4

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Articles | Volume 22, issue 4

https://doi.org/10.5194/nhess-22-1267-2022

Articles | Volume 22, issue 4

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Research article

11 Apr 2022

Research article |

| 11 Apr 2022

Rapid tsunami force prediction by mode-decomposition-based surrogate modeling

Kenta Tozato, Shinsuke Takase, Shuji Moriguchi, Kenjiro Terada, Yu Otake, Yo Fukutani, Kazuya Nojima, Masaaki Sakuraba, and Hiromu Yokosu

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

s_mori@irides.tohoku.ac.jp

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

https://orcid.org/0000-0002-8976-4503

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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Total article views: 1,627 (including HTML, PDF, and XML)

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Total: 1,627
Supplement: 88
BibTeX: 49
EndNote: 42

Views and downloads (calculated since 11 Apr 2022)

Month	HTML	PDF	XML	Total
Apr 2022	167	38	7	212
May 2022	50	10	0	60
Jun 2022	39	10	0	49
Jul 2022	45	9	2	56
Aug 2022	64	8	0	72
Sep 2022	27	11	1	39
Oct 2022	24	11	0	35
Nov 2022	40	10	0	50
Dec 2022	25	6	1	32
Jan 2023	10	5	0	15
Feb 2023	19	13	1	33
Mar 2023	21	8	1	30
Apr 2023	20	9	1	30
May 2023	33	14	3	50
Jun 2023	15	5	0	20
Jul 2023	14	10	2	26
Aug 2023	20	13	3	36
Sep 2023	16	10	0	26
Oct 2023	14	15	1	30
Nov 2023	11	6	0	17
Dec 2023	33	7	0	40
Jan 2024	111	9	1	121
Feb 2024	25	16	3	44
Mar 2024	19	14	0	33
Apr 2024	35	16	5	56
May 2024	42	8	1	51
Jun 2024	41	9	3	53
Jul 2024	14	7	4	25
Aug 2024	26	6	3	35
Sep 2024	12	6	1	19
Oct 2024	23	22	0	45
Nov 2024	11	1	0	12
Dec 2024	19	5	0	24
Jan 2025	30	32	3	65
Feb 2025	23	24	2	49
Mar 2025	34	2	1	37

Cumulative views and downloads (calculated since 11 Apr 2022)

Month	HTML	PDF	XML	Total
Apr 2022	167	38	7	212
May 2022	50	10	0	60
Jun 2022	39	10	0	49
Jul 2022	45	9	2	56
Aug 2022	64	8	0	72
Sep 2022	27	11	1	39
Oct 2022	24	11	0	35
Nov 2022	40	10	0	50
Dec 2022	25	6	1	32
Jan 2023	10	5	0	15
Feb 2023	19	13	1	33
Mar 2023	21	8	1	30
Apr 2023	20	9	1	30
May 2023	33	14	3	50
Jun 2023	15	5	0	20
Jul 2023	14	10	2	26
Aug 2023	20	13	3	36
Sep 2023	16	10	0	26
Oct 2023	14	15	1	30
Nov 2023	11	6	0	17
Dec 2023	33	7	0	40
Jan 2024	111	9	1	121
Feb 2024	25	16	3	44
Mar 2024	19	14	0	33
Apr 2024	35	16	5	56
May 2024	42	8	1	51
Jun 2024	41	9	3	53
Jul 2024	14	7	4	25
Aug 2024	26	6	3	35
Sep 2024	12	6	1	19
Oct 2024	23	22	0	45
Nov 2024	11	1	0	12
Dec 2024	19	5	0	24
Jan 2025	30	32	3	65
Feb 2025	23	24	2	49
Mar 2025	34	2	1	37

Total article views: 1,107 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
623	459	25	1,107	12	7

HTML: 623
PDF: 459
XML: 25
Total: 1,107
BibTeX: 12
EndNote: 7

Views and downloads (calculated since 07 May 2021)

Month	HTML	PDF	XML	Total
Apr 2022	167	38	7	212
May 2022	50	10	0	60
Jun 2022	39	10	0	49
Jul 2022	45	9	2	56
Aug 2022	64	8	0	72
Sep 2022	27	11	1	39
Oct 2022	24	11	0	35
Nov 2022	40	10	0	50
Dec 2022	25	6	1	32
Jan 2023	10	5	0	15
Feb 2023	19	13	1	33
Mar 2023	21	8	1	30
Apr 2023	20	9	1	30
May 2023	33	14	3	50
Jun 2023	15	5	0	20
Jul 2023	14	10	2	26
Aug 2023	20	13	3	36
Sep 2023	16	10	0	26
Oct 2023	14	15	1	30
Nov 2023	11	6	0	17
Dec 2023	33	7	0	40
Jan 2024	111	9	1	121
Feb 2024	25	16	3	44
Mar 2024	19	14	0	33
Apr 2024	35	16	5	56
May 2024	42	8	1	51
Jun 2024	41	9	3	53
Jul 2024	14	7	4	25
Aug 2024	26	6	3	35
Sep 2024	12	6	1	19
Oct 2024	23	22	0	45
Nov 2024	11	1	0	12
Dec 2024	19	5	0	24
Jan 2025	30	32	3	65
Feb 2025	23	24	2	49
Mar 2025	34	2	1	37

Cumulative views and downloads (calculated since 07 May 2021)

Month	HTML	PDF	XML	Total
Apr 2022	167	38	7	212
May 2022	50	10	0	60
Jun 2022	39	10	0	49
Jul 2022	45	9	2	56
Aug 2022	64	8	0	72
Sep 2022	27	11	1	39
Oct 2022	24	11	0	35
Nov 2022	40	10	0	50
Dec 2022	25	6	1	32
Jan 2023	10	5	0	15
Feb 2023	19	13	1	33
Mar 2023	21	8	1	30
Apr 2023	20	9	1	30
May 2023	33	14	3	50
Jun 2023	15	5	0	20
Jul 2023	14	10	2	26
Aug 2023	20	13	3	36
Sep 2023	16	10	0	26
Oct 2023	14	15	1	30
Nov 2023	11	6	0	17
Dec 2023	33	7	0	40
Jan 2024	111	9	1	121
Feb 2024	25	16	3	44
Mar 2024	19	14	0	33
Apr 2024	35	16	5	56
May 2024	42	8	1	51
Jun 2024	41	9	3	53
Jul 2024	14	7	4	25
Aug 2024	26	6	3	35
Sep 2024	12	6	1	19
Oct 2024	23	22	0	45
Nov 2024	11	1	0	12
Dec 2024	19	5	0	24
Jan 2025	30	32	3	65
Feb 2025	23	24	2	49
Mar 2025	34	2	1	37

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Total article views: 2,734 (including HTML, PDF, and XML) Thereof 2,596 with geography defined and 138 with unknown origin.

Total article views: 1,627 (including HTML, PDF, and XML) Thereof 1,556 with geography defined and 71 with unknown origin.

Total article views: 1,107 (including HTML, PDF, and XML) Thereof 1,040 with geography defined and 67 with unknown origin.

Country	#	Views	%
United States of America	1	969	35
Japan	2	372	13
China	3	283	10
France	4	117	4
Germany	5	85	3

Country	#	Views	%
United States of America	1	481	29
China	2	217	13
Japan	3	208	12
France	4	92	5
undefined	5	54	3

Country	#	Views	%
United States of America	1	488	44
Japan	2	164	14
China	3	66	5
Germany	4	37	3
Ireland	5	33	2


Total:	0
HTML:	0
PDF:	0
XML:	0

969


Total:	0
HTML:	0
PDF:	0
XML:	0

481


Total:	0
HTML:	0
PDF:	0
XML:	0

488

Cited

Show all Final revised paper only Preprint only

8 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
Surrogate modeling for transient electrochemical potential analysis for SOFC using proper orthogonal decomposition M. Sato et al. 10.1016/j.ssi.2024.116642
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

7 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
Surrogate modeling for transient electrochemical potential analysis for SOFC using proper orthogonal decomposition M. Sato et al. 10.1016/j.ssi.2024.116642
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.

Sequential Bayesian Update to Detect the Most Likely Tsunami Scenario Using Observational Wave Sequences R. Nomura et al. 10.1029/2021JC018324

Latest update: 29 Mar 2025

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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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