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In this paper, the inundation depths, which are simulated and validated by a physically based two-dimensional model (FLO-2D), are used as a database for inundation forecasting.
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Therefore, the main goal of this study is to establish an inundation evaluation system suitable for all metropolitan areas in Taiwan in conjunction with the quantitative precipitation forecast technology developed by the Taiwan Typhoon and Flood Research Institute, which can be used for inundation forecast 24 h before the arrival of typhoons.
The strategy proposed by Abe and Imamura (2010) will be preferred for the inundation forecasting of near-field tsunamis.
Due to the lack of observed data of inundation maps, sufficient observed data are not available for developing inundation forecasting models.
In conclusion, the proposed two-stage forecasting approach is suitable and useful for improving the inundation forecasting during typhoons, especially for long lead times.
A two-stage inundation forecasting approach based on Support Vector Machine (SVM) is proposed to yield 1- to 6-h lead-time inundation maps during typhoons.
By adding tsunami inundation components, such as the inundation area and run-up heights to the TSB, our method can be expanded for real-time tsunami inundation forecasts.
In addition, to provide high-resolution tsunami inundation forecasts for a wide coastal zone, we use a pre-calculated database rather than on-demand forward simulation.
A hurricane storm surge test problem shows that a 10 m resolution, 12 h inundation forecast for a 40 km length of coastline can be completed in under 2 h using 512 processors.
We propose a new approach for real-time tsunami inundation forecasts that uses only tsunami data obtained by offshore ocean-bottom pressure gauges without using any seismological observation data.
For a real-time tsunami inundation forecast, some researchers have proposed new methods that use on-demand forward simulations with inverted tsunami source models (Tatsumi and Tomita 2013; Koshimura et al. 2014; Oishi et al. 2015).
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