AI-driven optimization for data-based decision-making and AR in disaster management

To better understand how to integrate renewable energy resources into disaster management plans, this chapter investigates data-driven decision-making. The study demonstrates how using advanced data analytics, augmented reality (AR) technologies, and modeling approaches may improve resilience and disaster response plans through renewable energy. AR technologies enable real-time visualization of critical information, such as renewable energy deployment points, energy consumption patterns, and resource allocation maps, overlaid directly on disaster-affected areas. This improves situational awareness and facilitates swift decision-making. The results highlight how crucial it is to use data effectively to optimize energy resources to assist recovery and emergency operations. One approach to deal with ambiguity and uncertainty in data is fuzzy logic. It is quite effective, for instance, when multicriteria decision-making (MCDM) problems are examined in which the options are evaluated concerning several, possibly conflicting, criteria. In this chapter, fuzzy logic in MCDM is discussed, focusing on its ability to work with vague information. Hence, using the terminology of terms and fuzzy sets, fuzzy logic enables decision-makers to formulate subjective preferences and uncertainties with much more ease than conventional methods that deal with imprecise information. AR enhances this process by visually representing fuzzy logic-based analyses, such as trade-offs between energy sources or the impact of renewable energy solutions on disaster resilience. Additionally, AR provides both qualitative and quantitative information and captures complex interactions of criteria through intuitive interfaces. The central fuzzy logic techniques like fuzzy inference systems, preference relations, and aggregation operators are very helpful in checking the trade-offs between conflicting criteria, incorporating experts’ opinions, and creating resilient solutions for any decision. Case studies in disaster management, engineering, finance, and healthcare show the real utility of combining fuzzy logic with AR technologies. Fuzzy logic-based methods and AR tools are sound candidates for robust decision support by synthesizing heterogeneous inputs, resolving uncertainty, and enhancing communication of complex insights to diverse stakeholders. The chapter offers some trends and challenges in combining fuzzy logic with optimization techniques and emerging technologies like AR. It demonstrates how these systems improve decision-making processes by making them more rational, operational, and visually intuitive.