The Use of Decision Support in Search and Rescue: A Systematic Literature Review

Whenever natural and human-made disasters strike, the proper response of the concerned authorities often relies on search and rescue services. Search and rescue services are complex multidisciplinary processes that involve several degrees of interdependent assignments. To handle such complexity, decision support systems are used for decision-making and execution of plans within search and rescue operations. Advances in data management solutions and artificial intelligence technologies have provided better opportunities to make more efficient and effective decisions that can lead to improved search and rescue operations. This paper provides findings from a bibliometric mapping and a systematic literature review performed to: (1) identify existing search and rescue processes that use decision support systems, data management solutions, and artificial intelligence technologies; (2) do a comprehensive analysis of existing solutions in terms of their research contributions to the investigated domain; and (3) investigate the potential for knowledge transfer between application areas. The main findings of this review are that non-conventional data management solutions are commonly used in land rescue operations and that geographical information systems have been integrated with various machine learning approaches for land rescue. However, there is a gap in the existing research on search and rescue decision support at sea, which can motivate future studies within this specific application area.

Automated summary

Whenever disasters occur, search and rescue services are crucial for proper response. Decision support systems using data management solutions and artificial intelligence technologies have improved the efficiency and effectiveness of search and rescue operations. This paper presents findings from a study that identified existing search and rescue processes, analyzed their research contributions, and explored knowledge transfer potential. The review highlights the use of unconventional data management solutions and integration of geographical information systems with machine learning in land rescue operations, but suggests a research gap in search and rescue decision support at sea.