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https://doi.org/10.5194/nhess-2020-373
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-373
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  16 Nov 2020

16 Nov 2020

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This preprint is currently under review for the journal NHESS.

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

Rimali Mitra1, Hajime Naruse1, and Shigehiro Fujino2 Rimali Mitra et al.
  • 1Division of Earth and Planetary Sciences, Graduate School of Science, Kyoto University, Kitashirakawa Oiwakecho, Kyoto, 606-8502, Japan
  • 2Faculty of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, Japan

Abstract. The 2004 Indian Ocean tsunami caused major topographic changes that resulted in significant economic losses and a large number of fatalities in the coastal areas. The estimation of tsunami flow conditions using inverse models has become a fundamental aspect of disaster mitigation and management. Here, in relation to the 2004 Indian Ocean tsunami, a case study involving the Phra Thong island in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The inverse analysis reconstructed the values of flow conditions such as maximum inundation length, flow velocity and maximum flow depth, sediment concentration from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other models applied to areas in and around Phra Thong island, the predicted flow conditions were compared with the reported observed values and simulated results. The estimated depositional characteristics such as volume per unit area and grain-size distribution, were in line with the measured values from the field survey. These qualitative and quantitative comparisons demonstrated that the DNN inverse model is a potential tool for estimating the characteristics of modern tsunamis.

Rimali Mitra et al.

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Model code and software

DNN inverse model for 2004 Indian Ocean tsunami, Phra Thong island, Thailand Rimali Mitra, Hajime Naruse, and Shigehiro Fujino https://doi.org/10.5281/zenodo.4075137

Rimali Mitra et al.

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Short summary
A case study was conducted at the Phra Thong island, Thailand caused by 2004 Indian Ocean tsunami using 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.
A case study was conducted at the Phra Thong island, Thailand caused by 2004 Indian Ocean...
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