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

Wajeeha Nasar, Ricardo da Silva Torres, Odd Erik Gundersen, A. T. Karlsen
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引用次数: 1

Abstract

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.
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决策支持在搜救中的应用:系统文献综述
每当天灾人祸袭来,有关当局的适当反应往往依赖于搜救服务。搜索和救援服务是复杂的多学科过程,涉及不同程度的相互依存任务。为了处理这种复杂性,在搜救行动中,决策支持系统被用于决策和执行计划。数据管理解决方案和人工智能技术的进步为做出更高效和有效的决策提供了更好的机会,从而改善了搜索和救援行动。本文提供了文献计量图和系统文献综述的研究结果:(1)确定使用决策支持系统、数据管理解决方案和人工智能技术的现有搜索和救援过程;(2)根据现有解决方案对所研究领域的研究贡献,对其进行综合分析;(3)研究应用领域间知识转移的潜力。本综述的主要发现是非常规数据管理解决方案通常用于土地救援行动,并且地理信息系统已与各种土地救援机器学习方法相结合。然而,现有的海上搜救决策支持研究还存在空白,这可以激励未来在这一特定应用领域的研究。
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