Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery
arXiv:2606.06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models. This work introduces a physics-guided deep learning framework that integrates multi-modal remote sensing (Sentinel-1 SAR, Sentinel-2 optical imagery, and DEM-derived terrain features) with constraints from the depth-averaged shallow water equations (SWE). The proposed hybrid architecture combines a UNet to capture fine-scale spatial detai
The increasing frequency and intensity of extreme weather events, coupled with advancements in deep learning and satellite imagery, are driving the urgent need for more effective flood prediction tools.
Accurate flood prediction is crucial for disaster preparedness, infrastructure planning, and mitigating economic losses, directly impacting human safety and regional stability.
Flood prediction can become significantly more precise and localized, moving beyond traditional models by integrating physics-based constraints with advanced AI to handle complex, heterogeneous environments.
- · Emergency services
- · Insurance companies
- · Urban planners
- · Agriculture sector
- · Communities in flood-prone areas (if not adequately prepared)
- · Legacy flood modeling firms (if slow to adapt)
Improved early warning systems will reduce flood-related casualties and property damage.
Better flood mapping will inform more resilient infrastructure development and revised zoning laws.
The integration of multi-modal sensing and AI-guided physics could set a new standard for environmental hazard prediction beyond just floods, impacting climate adaptation strategies broadly.
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Read at arXiv cs.LG