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- W4206542053 abstract "Abstract. Tsunamis rarely occur in a specific area, and their occurrence is highly uncertain. Generated from their sources in deep water, they occasionally undergo tremendous amplification over decreasing water depth to devastate low-lying coastal areas. Despite the advancement of computational power and simulation algorithms, there is a need for novel and rigorous approaches to efficiently predict coastal amplification of tsunamis during different disaster management phases, such as tsunami risk assessment and real-time forecast. This study presents convolution kernels that can instantly predict onshore waveforms of water surface elevation and flow velocity from observed/simulated wavedata apart from the shore. Kernel convolution involves isolating an incident-wave component from the offshore wavedata and transforming it into the onshore waveform. Moreover, unlike previous derived ones, the present kernels are based on shallow-water equations with a damping term and can account for tsunami attenuation on its path to the shore with a damping parameter. Kernel convolution can be implemented at a low computational cost compared to conventional numerical models that discretise the spatial domain. The prediction capability of the kernel method was demonstrated through application to real-world tsunami cases." @default.
- W4206542053 created "2022-01-25" @default.
- W4206542053 creator A5012971227 @default.
- W4206542053 date "2021-02-15" @default.
- W4206542053 modified "2023-10-17" @default.
- W4206542053 title "Tsunami propagation kernel and its applications" @default.
- W4206542053 doi "https://doi.org/10.5194/nhess-2021-30" @default.
- W4206542053 hasPublicationYear "2021" @default.
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