Disaster Management Support System Prototype Design Evolution Based on UX Testing

Simões-Marques, Mário; Mendonça, Pedro; Figueiredo, Diogo Hilgemberg; Nunes, Isabel L.

doi:10.1007/978-3-030-20040-4_34

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…For instance, fire emergencies such as fire in a building or urban fire emergencies, and day-to-day emergencies that can be traffic accidents, and industrial accidents. Natural and human-made disasters [10][11][12][13][14]17,36,42,63,64,72,73,80,81,83,84,[89][90][91][92]98,99] The articles where the authors contemplate both types of disasters. It means that the presented solutions in these articles work in both types of disasters.…”

Section: Disasters Descriptionmentioning

confidence: 99%

“…For example, Ni et al [59], Widagdo et al [78], and Sarma et al [87] presented post-disaster solutions using various relational databases for the redistribution of resources and estimation of damages. Simoes-Marques et al [72] used the UCD approach to employ a data warehouse as a disaster management support system. Similarly, Fleischhauer et al [77] utilized OpenStreetMap and GIS with a data lake for disaster management.…”

Section: Disasters Descriptionmentioning

confidence: 99%

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

Nasar

Torres

Gundersen

et al. 2023

IJGI

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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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