Articles | Volume 21, issue 5
Nat. Hazards Earth Syst. Sci., 21, 1667–1683, 2021
Nat. Hazards Earth Syst. Sci., 21, 1667–1683, 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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Cited articles

Abdi, H. and Williams, L. J.: Jackknife, in: Salkind, N., Encyclopedia of Research Design, Thousand Oaks, CA, Sage, 655–660,, 2010. a
Abe, T., Goto, K., and Sugawara, D.: Relationship between the maximum extent of tsunami sand and the inundation limit of the 2011 Tohoku-oki tsunami on the Sendai Plain, Japan, Sediment. Geol., 282, 142–150, 2012. a
Andrade, V., Rajendran, K., and Rajendran, C.: Sheltered coastal environments as archives of paleo-tsunami deposits: Observations from the 2004 Indian Ocean tsunami, J. Asian Earth Sci., 95, 331–341, 2014. a
Brill, D.: The Tsunami History of Southwest Thailand: Recurrence, Magnitude and Impact of Palaeo-tsunamis Inferred from Onshore Deposits, PhD thesis, Universitäts-und Stadtbibliothek Köln, Köln, 2012. a
Brill, D., Klasen, N., Brückner, H., Jankaew, K., Scheffers, A., Kelletat, D., and Scheffers, S.: OSL dating of tsunami deposits from Phra Thong Island, Thailand, Quat. Geochronol., 10, 224–229, 2012a. a
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.
Final-revised paper