An efficient 2-D flood inundation modelling based on a data-driven approach

Abstract

Study region: The plain areas of Yilan County in Taiwan. Study focus: The present study employed hourly precipitation and inundation simulation data from a 5-km resolution HiRAM-WRF (High-Resolution Atmospheric Model-Weather Research and Forecasting) downscaled model and a 40-m resolution SOBEK model to construct a data-driven two-dimensional flood inundation model (D2DFIM) based on the support vector regression (SVR) approach. New hydrological insights for the region: The dataset comprised 149 rainfall events and corresponding inundation events. We extracted the inundated and interpolated rainfall grid points distributed across Yilan County, Taiwan, from the SOBEK and HiRAM-WRF models. The D2DFIM (data-driven two-dimensional flood inundation model) utilized the parallel computing approach in the training phase, saving 90% of the elapsed time. The comparisons between the data-driven and physical-based models indicate that the D2DFIM could perform better in flood simulation if the input features comprised previous inundation depth and the last three time steps of rainfall information. Regarding the minimal required number of training samples, we found almost no significant difference in the flooding simulation once the training samples equalled or exceeded 50. Additionally, the elapsed time of D2DFIM is 9–10 times faster than that of SOBEK. We validated the well-developed D2DFIM using three historical inundated events induced by typhoons in Yilan County; the flood extents derived from the D2DFIM agreed well with the survey results.

Keywords

• Flood inundation simulation
• Data-driven approach
• Support vector regression
• HiRAM-WRF model
• SOBEK model