AI-Powered Framework for Strengthening Climate Resilience in Urban Infrastructure Systems
Abstract
AI as a resilience enabler based on predictive modeling and adaptive decision-making is what this research is going to propose its framework in order to enhance urban infrastructure system’s climate risk resilience. Unlike other climate danger models governed by legacy sources of data that remain running in silos, this process combines various supply of information, such as satellite image, IoT sensor networks, and socio financial indicators, into a single and context-aware gadget. Essentially, the model incorporates a hybrid ensemble of ML and DL models, including convolutional neural networks, spatiotemporal models, for the prediction of urban-specific climate hazards like the flood, heat waves, and infrastructure stress. One of the special characteristics of this framework is that this feedback loop is dynamic meaning that it keep learning and continually improve the model in response to new environmental and infrastructural conditions it is occurring in real time. The suggested model especially stands out because it is scalable, flexible and applicative in the real world, presenting an actual progress advancement in urban climate imperviousness systems and judicious city planning.