The Next Generation of Disaster Management and Relief Planning: Immersive Analytics Based Approach

Toujani, Radhia; Chaabani, Yasmine; Dhouioui, Zeineb; Bouali, Hanen

doi:10.1007/978-3-319-93596-6_6

Cited by 5 publications

(6 citation statements)

References 14 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%

“…Lopez et al [60] and Li et al [89] proposed distributed databases for training of decisionmaking agents and situational understanding using a view-invariant CNN model for emergency scenarios. Nunavath and Prinz [65], Kabir et al [82], and Toujani et al [84] all addressed fire emergencies using NoSQL. Kumar et al [36] and Zhang et al [46] presented data acquisition based on UAVs for emergency response and recovery.…”

Section: Disasters Descriptionmentioning

confidence: 99%

“…The studies by [36,38,46] utilized supervised learning, whereas the study by [67] used unsupervised learning. The articles by [60,66,73] proposed solutions based on RL to address emergency response processes, and the ones by [71,76,84,91,92,99] employed other ML techniques. Ahmed et al [71] used ML for multiple regression analysis to visualize the preparedness of emergency response and medical care.…”

Section: Disasters Descriptionmentioning

confidence: 99%

“…Gokaraju et al [76] implemented ML with statistical methods to collect sensor data and increase the performance efficiency of emergency preparedness and disaster management in cases such as landslides and tornadoes. Toujani et al [84] used ML in Microsoft Power BI to analyze data in order to address situational awareness during fire emergencies. Ejaz et al [91] implemented an ML algorithm for energy-efficient task scheduling of UAVs to find the optimal path with the lowest energy consumption.…”

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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Section: Disasters Descriptionmentioning

confidence: 99%

Section: Disasters Descriptionmentioning

confidence: 99%

Section: Disasters Descriptionmentioning

confidence: 99%

Section: Disasters Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nasar

Torres

Gundersen

et al. 2023

IJGI

View full text Add to dashboard Cite

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“…BDA seems to be the choice in the recent research efforts to deal with such issues. The authors in [56] used Hadoop platform and machine learning techniques to perform sentiment analysis on big social data. Support vector machine algorithm was used for the sentiment classifications and an interactive visualization mechanism was deployed to provide information for prompt decision-making.…”

Section: Crowdsourcingmentioning

confidence: 99%

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

et al. 2019

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The recent development of big data analytics (BDA) and the Internet of Things (IoT) technologies create a huge opportunity for both disaster management systems and disaster-related authorities (emergency responders, police, public health, and fire departments) to acquire state-of-the-art assistance and improved insights for accurate and timely decision-making. The motivation behind this research is to pave the way for effective utilization of the available opportunities that the BDA and IoT collaboratively offer to predict, understand and monitor disaster situations. Most of the conventional disaster management systems lack the support for multiple new data sources and real-time big data processing tools that can assist decision makers with quick and accurate results. This paper highlights the importance of BDA and IoT for disaster management and investigates recent studies directed towards the same. We classify a thematic taxonomy with several related attributes and inspect the prevalent solutions to propose a conceptual reference model for the deployment of BDA-and IoT-based disaster management environments. The reference model with its proposed integrated parameters can provide guidelines to harvest, transmit, manage, and analyze disaster data from various data sources to deliver updated and valuable information for disaster management. We also enumerate some important use cases from a disaster management perspective. Finally, we highlight the main research challenges that need to be addressed in such an important field of research. INDEX TERMS Big data analytics, data sources, disaster communications, disaster management, Internet of Things, reference model, taxonomy.

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Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of R

et al. 2022

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Decision making is vital for the management of all organizations. For this reason, data analysis has become one of the fastest-growing technologies when it comes to generating information and knowledge about data generated by organizations. However, data generation is not limited to traditional sources. On the contrary, emerging technologies and social networks have become non-traditional sources that provide large volumes of data that can be exploited using different data analysis methods. Here, the objective is to determine the feelings of the population toward a brand, a product, or a service and to even identify the reactions of people to events and trends generated in their environment. Sentiment analysis, for organizations and social groups, has become a necessity that must be covered to identify the acceptance of an idea or its management. Therefore, this work proposes a method for the analysis of sentiment in social networks in such a way that it adapts to the needs of organizations or sectors, and the acceptance or rejection of the population can be efficiently identified from what is exposed in a social network.

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The Next Generation of Disaster Management and Relief Planning: Immersive Analytics Based Approach

Cited by 5 publications

References 14 publications

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

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

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

Proposal of a Method for the Analysis of Sentiments in Social Networks with the Use of R

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