Predicting Flood Inundation Depth Based-on Machine Learning and Numerical Simulation

Recent advances in earth observation and machine learning have enabled rapid estimation of flooded areas following catastrophic events such as torrential rains and riverbank overflows. However, estimating the actual inundation depth remains a challenge since it often requires detailed numerical simulation. This paper presents a methodology for predicting the inundation from remote sensing derived information by coupling deep learning and numerical simulation. We generate a large dataset of flood depth inundations considering several heavy rain conditions in four independent target areas. We propose a CNN-based regression framework. Our experiment demonstrates that our methodology can predict inundation depth on a separate target area not included during training, demonstrating great generalization ability.