Abstract This work gives a concise literature review of the previous studies on disaster management through the use of Artificial Intelligence (AI). It is supported by evidence from 96 peer-reviewed articles sourced from multiple academic databases, including IEEE Xplore, Elsevier, Wiley and Springer. It discusses two broad topics: The phases of the disaster management cycle (Preparedness, Response, and Recovery) and a three-stage time frame (Primitive: 2000–2010, Transitional: 2011–2016, Modern: 2017-present). The research is conducted through visual graphics to demonstrate variation in research activity in different regions, periods, and phases of disasters. It involves a bibliometric co-occurrence analysis using VOSviewer, aimed at obtaining important thematic clusters. Tables focus on comparing the performance of various AI methods applied to particular disaster-related tasks. The findings indicate the obvious replacement of conventional rule-based solutions with more sophisticated deep learning solutions. It throws light on existing gaps and recent developments by describing methodological trends, outcomes of performance and future directions of research.
Muthukumar et al. (Wed,) studied this question.