Articles | Volume 21, issue 5
Nat. Hazards Earth Syst. Sci., 21, 1667–1683, 2021
https://doi.org/10.5194/nhess-21-1667-2021
Nat. Hazards Earth Syst. Sci., 21, 1667–1683, 2021
https://doi.org/10.5194/nhess-21-1667-2021

Research article 31 May 2021

Research article | 31 May 2021

Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model

Rimali Mitra et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (further review by editor and referees) (15 Jan 2021) by Maria Ana Baptista
AR by Rimali Mitra on behalf of the Authors (15 Feb 2021)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (further review by editor and referees) (04 Mar 2021) by Maria Ana Baptista
ED: Referee Nomination & Report Request started (04 Mar 2021) by Maria Ana Baptista
RR by Pedro Costa (18 Mar 2021)
ED: Publish as is (26 Mar 2021) by Maria Ana Baptista

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Rimali Mitra on behalf of the Authors (20 May 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (26 May 2021) by Maria Ana Baptista
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Short summary
A case study on the 2004 Indian Ocean tsunami was conducted at the Phra Thong island, Thailand, using a deep neural network (DNN) inverse model. The model estimated tsunami characteristics from the deposits at Phra Thong island. The uncertainty quantification of the result was evaluated. The predicted flow conditions and the depositional characteristics were compared with the reported observed values. This DNN model can serve as an essential tool for tsunami hazard mitigation at coastal cities.
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