AI Alignment of Disaster Resilience Management Support Systems

This paper presents an application of Artificial Intelligence (AI) prospective studies to determine the most suitable AI technologies for implementation in Disaster Resilience Management Support Systems (DRMSSs). The pivotal role in our approach is played by the security needs analysis in the context of most common natural disasters and their co-occurrence with other threats. The AI trends and scenarios to align with are derived according to foresight principles. We apply expert knowledge elicitation and fusion techniques as well as a control model of technology dynamics. The pre-assessments of security needs and technological evolution prospects are combined to rank and select the most prospective AI methods and tools. Long-term ex-ante impact assessment of future disaster resilience improvements resulting from different DRMSS implementations, allows for the identification of the most suitable AI deployment variant. The target market of the DRMSSs under study includes industrial corporations and urban critical infrastructures, to become part of their Industry 4.0 ecosystems. The models of protected area and its environment are continually updated with visual monitoring and other sensors embedded in the Industrial Internet-of-Things infrastructure. The software architecture of DRMSS focuses on model-based decision support that applies fuzzy-stochastic uncertainty and multicriteria optimization. The business processes behind the AI alignment follow these goals. In the conclusion, we will show that DRMSS allows stakeholders to reach social, technological, and economic objectives simultaneously.