As the Gulf Coast heads towards the most active stretch of Atlantic hurricane season – August to September – forecasters warn that the area could face increased storm activity this year, spurring warm seawater and climate change. Researchers at Rice University are at the forefront of using artificial intelligence (AI) to address these urgent climate challenges. The Climate Risk and Urban Resilience research group, led by private and environmental engineering scholar James Dos Golin, and supported by the Ken Kennedy Institute, unites climate scientists, engineers and AI experts to develop transformative solutions, including:
- Extreme weather modeling and disaster response
- Climate risk forecasts and risk assessments
- Flood modeling and mitigation strategies
- City energy and transport resilience
Traditional hazard models offer valuable insights, but often have limited resolution, efficiency, and adaptability to different regions. AI4UrbanResilience integrates AI and machine learning with physics-based models to generate open source, high resolution, and computationally efficient tools to manage complex, interconnected systems under extreme weather stress. Key areas of focus include:
- Synthetic Hazard Generation: Generates a large AI-generated dataset of realistic synthetic weather patterns for urban downscaling.
- Infrastructure System Response: Assessing the dangerous impact on critical systems with AIL and optimized methods.
- Multi-scale Earth Observation and Data Assimilation: Combine a variety of data sets to improve model accuracy and enable real-time initialization.
- Reliability and Verification: Ensuring the transparency and reliability of physics-based AI through rigorous evaluation.
This work is consistent with Rice's initiative to position Universion as an AI-powered nexus for climate adaptation, creating a sustainable future. By connecting domain experts with AI researchers, the AI4UrbanResilience group aims to help communities across Houston and around the world prepare, endure and recover from climate-driven disasters.
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