scholarly journals Comment to nhess-2020-373 - Reconstruction of flow conditions from 2004 Indian Ocean tsunami deposits at the Phra Thong island using a deep neural network inverse model - by Mitra et al.

Author(s):  
Pedro Costa
2021 ◽  
Vol 21 (5) ◽  
pp. 1667-1683
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino

Abstract. The 2004 Indian Ocean tsunami caused 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, a case study involving the Phra Thong island, which was affected by the 2004 Indian Ocean tsunami, in Thailand was conducted using inverse modeling that incorporates a deep neural network (DNN). The DNN inverse analysis reconstructed the values of flow conditions such as maximum inundation distance, flow velocity and maximum flow depth, as well as the sediment concentration of five grain-size classes using the thickness and grain-size distribution of the tsunami deposit from the post-tsunami survey around Phra Thong island. The quantification of uncertainty was also reported using the jackknife method. Using other previous 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 physical characteristics of modern tsunamis.


2020 ◽  
Author(s):  
Rimali Mitra ◽  
Hajime Naruse ◽  
Shigehiro Fujino

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.


2018 ◽  
Author(s):  
◽  
Musa Al'ala ◽  
Hermann M. Fritz ◽  
Mirza Fahmi ◽  
Teuku Mudi Hafli

Abstract. After more than a decade of recurring tsunamis, identification of tsunami deposits, a part of hazard characterization, still remains a challenging task not fully understood. The lack of sufficient monitoring equipment and rare tsunami frequency are among the primary obstacles that limit our fundamental understanding of sediment transport mechanisms during a tsunami. The use of numerical simulations to study tsunami-induced sediment transport was rare in Indonesia until the 2004 Indian Ocean tsunami. This study aims to couple two hydrodynamic numerical models in order to reproduce tsunami-induced sediment deposits, i.e., their locations and thicknesses. Numerical simulations were performed using the Cornell Multi-Grid Coupled Tsunami Model (COMCOT) and Delft3D. This study reconstructed tsunami wave propagation from its source using COMCOT, which was later combined with Delft3D to map the location of the tsunami deposits and calculate their thicknesses. Two Dimensional-Horizontal (2DH) models were used as part of both simulation packages. Lhoong, in the Aceh Besar District, located approximately 60 km southwest of Banda Aceh, was selected as the study area. Field data collected in 2015 and 2016 validated the forward modeling techniques adopted in this study. However, agreements between numerical simulations and field observations were more robust using data collected in 2005, i.e., just months after the tsunami (Jaffe et al., 2006). We conducted pit (trench) tests at select locations to obtain tsunami deposit thickness and grain size distributions. The resulting numerical simulations are useful when estimating the locations and the thicknesses of the tsunami deposits. The agreement between the field data and the numerical simulations is reasonable despite a trend that overestimates the field observations.


2010 ◽  
Vol 230 (3-4) ◽  
pp. 95-104 ◽  
Author(s):  
Dan Matsumoto ◽  
Toshihiko Shimamoto ◽  
Takehiro Hirose ◽  
Jagath Gunatilake ◽  
Ashvin Wickramasooriya ◽  
...  

Tsunamiites ◽  
2008 ◽  
pp. 127-144
Author(s):  
K. Goto ◽  
F. Imamura ◽  
N. Keerthi ◽  
P. Kunthasap ◽  
T. Matsui ◽  
...  

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