Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation

The flood-prone city of Zaio in Morocco necessitates a precise, fast, real-time flood depth mapping model due to its recurrent flood history. Whether it's the exclusive prediction of one flood category, relying on hard-to-measure inputs like flood hydrographs, or employing less accurate training methods such as cellular automata models, the existing Convolutional Neural Network (CNN) models face limitations in predicting flood depth in a city whose flood dynamics are influenced by outer watersheds such as Zaio. This study addresses these issues by introducing a CNN tailored for real-time pluvial and fluvial flood depth mapping in Zaio at fine resolution (2 m). Training involved eight rainfall hyetographs, with four used for testing. The model's validation included three "unseen" rainfall distribution patterns. The proposed CNN seamlessly connects rainfall-runoff modeling and hydrodynamic 2D flood depth simulation, with a novelty of predicting both pluvial and fluvial flood depth, and reducing computational time by a significant 99.17%.