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

Maelaynayn El Baida (), Farid Boushaba, Mimoun Chourak and Mohamed Hosni
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Maelaynayn El Baida: National School of Applied Sciences of Oujda, Mohamed 1st University
Farid Boushaba: National School of Applied Sciences of Oujda, Mohamed 1st University
Mimoun Chourak: National School of Applied Sciences of Oujda, Mohammed 1st University
Mohamed Hosni: MOSI, L2M3S, ENSAM-Meknes, Moulay Ismail University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2024, vol. 38, issue 12, No 15, 4763-4782

Abstract: Abstract 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%.

Keywords: Urban flood; Convolutional neural network; Flood depth; Deep learning; Hydrodynamic modelling (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11269-024-03886-w

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