Urban Flood Resilience Under Extreme Precipitation: AI-Based Forecasting and Participatory Solutions Aligned with the UN Sustainable Development Goals

Precipitation extremes associated with climate change are exacerbating urban flood risks worldwide,  impacting rapidly urbanizing and socioeconomically vulnerable regions in the world. Addressing these challenges requires integrated approaches that link precipitation dynamics, urban hydrology, and community-centered adaptation. This work presents a multi-regional framework for precipitation-driven urban flood forecasting and mitigation aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 6 (Clean Water and Sanitation). The research is led by the United Nations University (UNU) Hub at the City College of New York—the first and only UNU hub in the United States dedicated to advancing urban resilience through science–policy integration.

The presented framework integrates artificial intelligence (AI) and high-resolution hydrometeorological data  across diverse urban environments. In Mumbai, India, machine-learning-based flood forecasting models are developed using high-resolution precipitation data, topography, land-use dynamics, and satellite observations to simulate real-time flood depths and extents during extreme rainfall events. These methods explicitly capture spatial variability in urban precipitation and evolving impervious surfaces, enhancing early warning capabilities in one of the world’s most flood-prone megacities.

The transferability of the methodology is demonstrated at different urban communities, including Mumbai, New York City, and the Caribbean. In New York City, precipitation-driven flash flood alert systems estimate real-time inundation risks during short-duration, high-intensity rainfall events. In Puerto Rico and the U.S. Virgin Islands, high-resolution inland flood risk maps are generated by integrating Depth–Duration–Frequency (DDF) precipitation relationships with terrain, soil, and land-use data, enabling the identification of flood hotspots under both current and projected rainfall regimes.

Beyond forecasting, the study advances a participatory framework for implementing nature-based solutions (NBS) in rural areas of Puerto Rico. By incorporating social vulnerability indicators and engaging local stakeholders, the approach supports equitable, community-driven flood mitigation strategies that enhance resilience actions.

Overall, this work demonstrates how precipitation-focused urban hydrology and  AI-driven forecasting can be  applied across global contexts to reduce flood risk, provide climate resilience, and facilitate the UN SDGs in both the Global South and developed urban regions.