An integrated two-stage support vector machine approach to forecast inundation maps during typhoons

During typhoons, accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation. Due to the lack of observed data of inundation maps, sufficient observed data are not available for developing inundation forecasting models. 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. 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. In the first stage (point forecasting), the proposed approach not only considers the rainfall intensity and inundation depth as model input but also simultaneously considers cumulative rainfall and forecasted inundation depths. In the second stage (spatial expansion), the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps. The results clearly indicate that the proposed approach effectively improves the forecasting performance and decreases the negative impact of increasing forecast lead time. Moreover, the proposed approach is capable of providing accurate inundation maps for 1- to 6-h lead times. In conclusion, the proposed two -stage forecasting approach is suitable and useful for improving the inundation forecasting during typhoons, especially for long lead times. (C) 2017 Elsevier B.V. All rights reserved.