Abstract

This paper explores how a form of artificial intelligence for IT operations (AIOps) might be leveraged to increase disaster resilience by enabling enhanced real-time infrastructure monitoring and optimized incident response on the network layer in case. It seeks to understand how the combination of AI-driven predictive analytics, multi-source data (satellite-based imagery and IoT with geospatial systems), and edge computing can help dramatically improve decision-making during such times. Reassuringly, the authors reveal how Explainable AI (XAI) plays a crucial role in ensuring solutions are trusted to address challenges like real-time processing or scalability as well as any security hazards. Looking further ahead, the paper discusses forthcoming trends such as autonomous response systems appearing in the wild, deep learning becoming pervasive to enable predictive management capabilities, and adaptive decision-making frameworks being widely implemented. Ideally, this aims to better situation awareness and reaction times—thus increasing the reliability of infrastructure across disaster-prone areas.