SPATIALLY EXPLICIT MACHINE LEARNING MODEL FOR FLOOD INUNDATION AUGMENTED WITH HYDRODYNAMIC MODELING

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 As extreme rainfall and flood events occur more frequently in the past years, the need for predicting detailed flood extent in inundation areas has been increasing. A machine learning model was proposed in this study aiming for the rapid spatial explicit flood prediction of inundation areas. The training process was performed with simulation results from a well-established hydrodynamic model, and investigations of incorporating predictors of water depths from past time steps were conducted. Evaluation of the model was first performed by numerical simulations and a case study was carried out. The model successfully predicted the water depth of the region with an overall coefficient of determination (R²) values greater than 0.9. While the accuracy varies with respect to land use, the model was able to represent the spatial differences in hydraulic properties within the area.