Artificial intelligence could become an important tool for helping the UAE prepare for floods, water stress and other climate-related risks. Researchers at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) have developed the Gulf Climate Agent (GCA), an AI framework designed around the environmental challenges of the Gulf.
The system combines regional climate information, policy documents, satellite data and specialised analytical tools. This region-specific approach can provide more useful climate decision support than relying on general-purpose AI models.
The Gulf faces a distinctive mix of extreme heat, flash floods, coastal changes and water stress. GCA is designed to analyse these challenges using multiple sources rather than depending only on information stored within an AI model.
The researchers say, “The system can connect questions with tools for rainfall analysis, hydrology, remote sensing and other climate applications. That could help experts examine rainfall patterns during flood events, monitor environmental changes and assess risks across specific locations.”
Its dataset contains about 200,000 question-and-answer pairs covering GCC climate policies, research and event-based information. The framework also uses visual and time-based environmental data to support tasks such as anomaly detection, forecasting and climate analysis.
MBZUAI earlier tested AI and computer vision for flood assessment following the major April 2024 Gulf storm. Researchers used satellite imagery to identify flooded roads and affected infrastructure in parts of Dubai, Abu Dhabi and Oman.
The team said, “ With sufficient historical data and more frequent satellite imagery, such systems could eventually help forecast how heavy rainfall may affect urban areas and support earlier warnings.”
The need for better forecasting is becoming more important as UAE rainfall patterns change. A 2026 UAE University study found that rainfall could become less frequent but more intense, increasing challenges for flood-risk assessment and water-resource planning.
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