PREDICTING FLOOD INUNDATION AREA BY RAINFALL-RUNOFF-INUNDATION MODEL EMULATOR

Abstract

 A rainfall-runoff-inundation model emulator was developed by machine larning using outputs from large ensemble climate simulations as a training dataset. The prediction of flood inundation area by the emulator was evaluated in the Omono river basin, which has been freaquently flooded in the past. The experimental results show that the inundation area near the river channel can be reproduced by the enulator with about 80 to 90% accuracy. We applied the machine to the actual flood inundation case in July 2017, and found that the result of prediction by the emulator is comparable to that by the results using rainfall-runoff-inundation model although the reproducibility differs according to the training data of machine learning.