NOWCAST AND FORECAST MODELING OF WATER-LEVEL PROFILES IN RIVERS BASED ON IMPROVED DIEX-FLOOD AND DEEP LEARNING

Abstract

 To reduce the computational load and improve the robustness of a flood prediction system (DIEX-Flood), which can estimate the water level longitudinal distribution in the current and future, we corrected the equation of DIEX-Flood and improved the data assimikation algorism. As a result of applying this method to Kinu river flood, it can calculate multiple cases of floods. Furthermore, we proposed a new flood forecasting method combining DIEX-Flood and deep learning. This prediction method can forecast water level longitudinal distribution in the future, so it makes possible to know spatial and temporal distribution of flood risk. This method can be expected accuracy improvement by assimilating water level data such as crisis management type water level gauges.