AI-ENABLED PREDICTIVE ANALYTICS FOR CLIMATE ADAPTATION AND COMMUNITY RESILIENCE: A SCALABLE FRAMEWORK FOR DATA-DRIVEN DECISION-MAKING IN VULNERABLE U.S. COASTAL POPULATIONS

Climate change has become a major problem to the populations in the coast of the United States and the rising sea
levels; extreme weather conditions and floods are threatening the lives and infrastructure. Although the risks have
been raised, there is still a lack of scalable, real-time, data-driven structures to support climate adaptation, as well as
improve community resilience. The article discusses the opportunity of AI-driven predictive analytics to deliver
useful information in climate adaptation on vulnerable coasts. With the combination of machine learning models,
geospatial data, and real-time environmental data, AI is able to enhance the accuracy of the climate risks prediction
and enable more effective decision-making processes. This paper focuses on AI-based flood risk management
systems, early warning, and distribution of resources on coastal communities. Results show that AI models have the
potential to become important in terms of timeliness and accuracy of predictions, enabling communities to execute
adaptive actions and enhance overall insights to climate-related dangers. Nevertheless, including data privacy,
model accuracy, and the necessity to collaborate across sectors are still present. The article ends with the realization
of the significance of scalable AI platforms to promote climate resilience in coastal areas and proposes the direction
of future research to expand the role of AI in climate action