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| Created: | Sep 28, 2025 at 4:49 a.m. (UTC) | |
| Last updated: | Sep 28, 2025 at 4:52 a.m. (UTC) | |
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Abstract
Accurate and timely flood forecasting is essential for enhancing resilience in coastal urban areas in the context of increasing frequency and intensity of rainfall, sea level rise and rapid urbanization. This study presents a hybrid deep learning-based surrogate model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to enable real-time spatiotemporal flood forecasting. The model leverages CNN to capture spatial features from inputs such as elevation and Topographic Wetness Index (TWI), while LSTM processes time-series inputs of rainfall and tide data to capture temporal features. The hybrid CNN-LSTM model was trained using the physics-based simulations obtained from the physics-based Two-dimensional Unsteady FLOW (TUFLOW) model for Norfolk, Virginia, and achieved high predictive accuracy across diverse flood-prone areas. It reduced computational time from four to six hours to under five minutes per event, enabling rapid flood inundation mapping and early warning. The model effectively captured both spatial flood extents and their temporal evolution across different flooding scenarios, providing forecasts at a 2.5 m spatial resolution and 15-minute temporal resolution, with a prediction horizon of one hour ahead. While challenges remain in terms of transferability and real-time data assimilation, this approach demonstrates strong potential for supporting operational flood risk management in coastal urban environments.
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