arXiv:2506.04281v2 Announce Type: replace Abstract: Compound flooding, driven by nonlinear interactions between multiple hydrometeorological factors, poses a significant challenge to hazard prevention. Existing forecasting approaches, whether physics-based or data-driven, often emphasize temporal patterns while underexploring how multiple interacting factors jointly shape flood dynamics. To address this problem, we conduct a large-scale data-driven analysis of compound flooding in South Florida, a typical area for compound flooding, by integrating tidal conditions, rainfall, groundwater stage,
The increasing frequency and intensity of extreme weather events, coupled with advancements in AI, make the application of data-driven models to compound flooding an urgent and timely endeavor.
Understanding and predicting compound flooding with greater accuracy is crucial for disaster preparedness, infrastructure planning, and economic stability in vulnerable regions worldwide.
The integration of diverse hydrometeorological factors into AI models offers a more comprehensive and accurate approach to flood prediction than previously available, moving beyond primarily temporal analyses.
- · AI/ML researchers
- · Coastal urban planners
- · Insurance industry
- · Emergency services
- · Regions unprepared for compound flooding
- · Traditional flood modeling approaches
Improved early warning systems and more effective disaster response for areas prone to compound flooding.
Reduced economic losses and displacement due to compound flood events, leading to more resilient coastal communities.
Potential for AI-driven adaptive infrastructure and land-use policies that dynamically respond to changing flood risks.
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Read at arXiv cs.LG