Rapid Generation of Emergency Response Plans for Unconventional Emergencies

Abstract

Unconventional emergencies can lead to unforeseen disastrous penalties. Due to their unrepeatable, complex, and unpredictable nature, it is generally hard to establish high-quality Emergency Response Plans (ERPs) for unconventional emergencies, thus posing great challenges for unconventional emergency response. This work proposes a rapid ERP generation approach for unconventional emergencies so as to provide support for emergency decision-making. The generation of ERPs is achieved by exploitation of existing ERPs that contain much emergency response experience. First, a number of ERPs are collected and structurally organized to construct an ERP repository. Then, the applicability of each ERP segment in the repository to a given unconventional emergency is evaluated by a proposed ERP similarity measure and emergency scenario matching mechanism, in which the semantic relevance and the scenario consistency of an ERP segment are taken into account, respectively. Applicable ERP segments are obtained for each section of ERPs, and combined to form a new ERP with the guidance of pre-defined ERP structure. Furthermore, we design an ERP assessment method and perform a case study on the proposed approach which presents encouraging experimental results.

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

“…Additionally, other technologies such as NLP, serious games, Petri nets, and multiobjective optimizations were found to be utilized in different data-centric domain categories to address SAR processes. For instance, Ni et al [59] used an NLP technique in developing an emergency response repository to build textual emergency response plans. Furthermore, Petri nets and workflow nets were presented in the context of cross-organizational emergency responsemodeling for better communication and resource distribution [62,85,90].…”

“…Table 8 and Figure 4 present the distribution of papers published over the years. With regard to the final data sample, the number of articles from 2017 is higher than in other years, and IEEE ACCESS remains the most influential source of publications for these articles [12,13,[59][60][61][62].…”

“…Natural disasters [7,39,40,59,60,69,71,[75][76][77][78]82,87,88,96] The articles where the category encompasses disasters caused by nature. e.g., floods, hurricanes, earthquakes, avalanches, and bushfires.…”

“…Most of these articles focused on land disasters, and most authors presented solutions for SAR processes based on UAVs and data. 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.…”

“…For example, Nunavath and Prinz [65] presented solutions based on serious games to visualize and analyze data collected from smart glasses and cameras, whereas Zeng et al [85], Duan et al [86], and Li et al [90] used Petri net models for emergency response modeling in fire emergencies. Sarma et al [87] proposed the concept of resource redistribution among affected areas in an emergency with the help of a multi-objective optimization model, and Ni et al [59] presented textual emergency response plans based on NLP.…”