Combining Deep Learning and Numerical Simulation to Predict Flood Inundation Depth

Current flood mapping methods combine remote sensing and machine learning technologies to estimate the inundation area. Although these methods have shown great success, they mainly focus on the flood extent without additional information on the inundation depth. However, knowing the inundation level can significantly benefit first responders and rescue efforts. Recent advances in machine learning have boosted the development of advanced methods for disaster management. This paper integrates modern convolutional neural network (CNN) models and physics-based numerical simulation to develop a novel framework for automatic flood inundation depth mapping. Our framework builds a synthetic training dataset using numerical flood simulation in four geographical regions. Then, it trains CNN models to understand the nonlinear relationship between inundation depth and topographic features. Our experiments, designed to evaluate the strength of our methodology in a real-world application, demonstrate that it can estimate flood depth with acceptable accuracy (Root-Mean-Squared Error=0.2) in unseen areas during training. These results indicate that a worldwide flood inundation mapping could be achieved by including key areas with representative topographic features.