AI-DRIVEN PREDICTIVE ANALYTICS FOR SMART INFRASTRUCTURE MANAGEMENT

Abstract

The rapid growth of urban populations has increased the demand for efficient infrastructure management in modern cities. Traditional infrastructure monitoring systems rely on manual inspection and reactive maintenance strategies, which often result in delays, inefficient resource utilization, and increased operational costs. Artificial Intelligence (AI) combined with Internet of Things (IoT) technologies provides new opportunities for improving infrastructure monitoring and predictive decision-making. The proposed framework collects environmental data such as rainfall intensity, water level, and drainage flow rate through IoT sensors deployed across urban areas. Machine learning models analyse the collected data to identify patterns and predict infrastructure risks such as flooding, drainage failures, and system overloads. Furthermore, the integration of cloud computing enables scalable data storage and real-time processing, enhancing the responsiveness of the system. Advanced analytics techniques allow continuous learning from historical and real-time data, improving prediction accuracy over time. The system also supports automated alert generation and decision support mechanisms for timely intervention by infrastructure authorities. Experimental evaluation demonstrates that AI-based models significantly improve prediction accuracy and enable proactive infrastructure maintenance. Overall, the proposed approach contributes to the development of smart, resilient, and sustainable urban infrastructure systems by reducing risks, optimizing maintenance strategies, and enhancing operational efficiency.