Toward a new backpressure-based framework to enhance situational awareness in disaster response

2017 ◽  
Author(s):  
Abdelbaset Kabou ◽  
Nadia Nouali-Taboudjemat ◽  
Omar Nouali
Keyword(s):  
Disaster Response ◽  
Situational Awareness

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

2021 ◽  
pp. 016555152110077
Author(s):  
Sulong Zhou ◽  
Pengyu Kan ◽  
Qunying Huang ◽  
Janet Silbernagel
Keyword(s):  
Social Media ◽  
Real Time ◽  
Language Processing ◽  
Disaster Response ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Situational Awareness ◽  
High Performing ◽  
Latent Topics ◽  
Dirichlet Allocation

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

scholarly journals Learning to Trust the Crowd: Validating ‘Crowd’ Sources for Improved Situational Awareness in Disaster Response

2016 ◽  
Vol 159 ◽  
pp. 141-147 ◽  
Author(s):  
Colby Howard ◽  
David Jones ◽  
Steven Reece ◽  
Antony Waldock
Keyword(s):  
Disaster Response ◽  
Situational Awareness

Digital Strategies to Improve Food Assistance in Disasters: A Scoping Review

2021 ◽  
pp. 1-10
Author(s):  
Nina M. Martin ◽  
Samantha M. Sundermeir ◽  
Daniel J. Barnett ◽  
Ellen J. I. van Dongen ◽  
Lori Rosman ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Scoping Review ◽  
Disaster Response ◽  
Situational Awareness ◽  
Food Assistance ◽  
Data Types ◽  
Client Choice ◽  
Scoping Literature Review ◽  
Emergency Food Assistance ◽  
Collection And Management

Abstract Objective: Modern digital strategies, including Internet of Things, machine learning, and mobile applications, have revolutionized situational awareness during disaster management. Despite their importance, no review of digital strategies to support emergency food security efforts has been conducted. This scoping review fills that gap. Methods: Keywords were defined within the concepts of food assistance, digital technology, and disasters. After the database searches, PRISMA guidelines were followed to perform a partnered, 2-round scoping literature review. Results: The search identified 3201 articles, and 26 articles met criteria and were included in the analysis. The data types used to describe the tools were text/opinion (42.3%), qualitative (23.1%), system architecture (19.2%), quantitative and qualitative (11.5 %), and quantitative (3.8%). The tools’ main functions were Resource Allocation (41.7%), Data Collection and Management (33%), Interagency Communications (15.4 %), Beneficiary Communications (11.5%), and Fundraising (7.7%). The platforms used to achieve these goals were Mobile Application (36%), Internet of Things (20%), Website (20%), and Mobile Survey (8%); 92% covered the disaster response phase. Conclusions: Digital tools for planning, situational awareness, client choice, and recovery are needed to support emergency food assistance, but there is a lack of these tools and research on their effectiveness across all disaster phases.

Moving from situational awareness to decisions during disaster response: Transition to decision making

2013 ◽  
Vol 11 (6) ◽  
pp. 423 ◽  
Author(s):  
Jeffrey A. Glick, PhD ◽  
Joseph A. Barbera, MD
Keyword(s):  
Decision Making ◽  
Decision Maker ◽  
Disaster Response ◽  
Situational Awareness ◽  
Initial Response ◽  
Structured Interview ◽  
New Paradigm ◽  
Emergency Situations ◽  
Disaster Situation ◽  
Data Elements

During major disasters, at what point in the decisional process do senior government officials transition from developing necessary situational awareness to perform decision making? This “transition to decision making” (TDM) concept was analyzed through a structured interview survey of 25 current and former US Federal Coordinating Officers (FCOs) and focused on their decision-making process during the initial response period in a Presidentially declared Stafford Act disaster. This analysis suggests that the TDM for these emergency leaders is influenced by the following five factors: 1) Analogue Factor: the decision maker’s previous knowledge and experience from analogous disaster situations; 2) New Paradigm Factor: the degree to which the disaster situation is very atypical to the decision maker due to hazard type and/or situation severity, 3) Data Capture Factor: the quality, amount, and speed of disaster situation data conveyed to the decision maker; 4) Data Integration Factor: the decision maker’s ability to integrate situational data elements into a mental framework/picture; and 5) Time Urgency Factor: the decision maker’s perception as to time available before a decision has to be made. The article describes the factors and graphs that how these may influence the timing of the TDM in four types of emergency situations faced by FCOs: 1) an analogue disaster, 2) a disaster situation that presents a new paradigm, 3) an intuitive disaster situation, and 4) a disaster requiring an urgent response.

An Innovative System to Enhance Situational Awareness in Disaster Response

2016 ◽  
Vol 13 (3) ◽  
pp. 301-327 ◽  
Author(s):  
Bukhtiar Mohsin ◽  
Friedrich Steinhäusler ◽  
Pierre Madl ◽  
Maximilian Kiefel
Keyword(s):  
Situation Awareness ◽  
Disaster Response ◽  
Situational Awareness ◽  
Dispersion Model ◽  
Research Effort ◽  
Plume Dispersion ◽  
End User ◽  
Core System ◽  
Multiple Sensors ◽  
Review Team

Abstract A collaborative decision support system has been developed to enhance situation awareness in case of complex disasters. It includes three modules: (1) UAV carrying multiple sensors; (2) Computer-based expert system (CES); (3) 2D-plume dispersion model. This research focuses on how contemporary technology, combined with commercial off-the-shelf-technology, can be used to fuse information and speed up decision-making. The system provides a context based common operational picture for first responders on scene, and incident commanders in control rooms. The research was conducted in close collaboration with different first response teams, to develop an end user oriented system. We present the design process the regulatory requirements, and the end user needs that will help future developers of such systems. We also highlight some of the areas where further research effort is required. The core system has been evaluated by local police and fire departments, and an EU review team including medical and firefighting practitioners.

scholarly journals A Disaster Response System based on Human-Agent Collectives

2016 ◽  
Vol 57 ◽  
pp. 661-708 ◽  
Author(s):  
Sarvapali D. Ramchurn ◽  
Trung Dong Huynh ◽  
Feng Wu ◽  
Yukki Ikuno ◽  
Jack Flann ◽  
...  
Keyword(s):  
Hurricane Katrina ◽  
Disaster Response ◽  
Participatory Design ◽  
Situational Awareness ◽  
Local Population ◽  
Field Trials ◽  
Terror Attacks ◽  
Response System ◽  
Human Agent ◽  
New Research

Major natural or man-made disasters such as Hurricane Katrina or the 9/11 terror attacks pose significant challenges for emergency responders. First, they have to develop an understanding of the unfolding event either using their own resources or through third-parties such as the local population and agencies. Second, based on the information gathered, they need to deploy their teams in a flexible manner, ensuring that each team performs tasks in The most effective way. Third, given the dynamic nature of a disaster space, and the uncertainties involved in performing rescue missions, information about the disaster space and the actors within it needs to be managed to ensure that responders are always acting on up-to-date and trusted information. Against this background, this paper proposes a novel disaster response system called HAC-ER. Thus HAC-ER interweaves humans and agents, both robotic and software, in social relationships that augment their individual and collective capabilities. To design HAC-ER, we involved end-users including both experts and volunteers in a several participatory design workshops, lab studies, and field trials of increasingly advanced prototypes of individual components of HAC-ER as well as the overall system. This process generated a number of new quantitative and qualitative results but also raised a number of new research questions. HAC-ER thus demonstrates how such Human-Agent Collectives (HACs) can address key challenges in disaster response. Specifically, we show how HAC-ER utilises crowdsourcing combined with machine learning to obtain most important situational awareness from large streams of reports posted by members of the public and trusted organisations. We then show how this information can inform human-agent teams in coordinating multi-UAV deployments, as well as task planning for responders on the ground. Finally, HAC-ER incorporates an infrastructure and the associated intelligence for tracking and utilising the provenance of information shared across the entire system to ensure its accountability. We individually validate each of these elements of HAC-ER and show how they perform against standard (non-HAC) baselines and also elaborate on the evaluation of the overall system.

Situational Awareness Technologies for Disaster Response

2008 ◽  
pp. 517-544 ◽  
Author(s):  
Naveen Ashish ◽  
Ronald Eguchi ◽  
Rajesh Hegde ◽  
Charles Huyck ◽  
Dmitri Kalashnikov ◽  
...  
Keyword(s):  
Disaster Response ◽  
Situational Awareness

The Shared Information Platform for Disaster Management –The Research and Development Regarding Technologies for Utilization of Disaster Information–

2019 ◽  
Vol 14 (2) ◽  
pp. 279-291 ◽  
Author(s):  
Yuichiro Usuda ◽  
Takashi Matsui ◽  
Hiroshi Deguchi ◽  
Toshikazu Hori ◽  
Shingo Suzuki ◽  
...  
Keyword(s):  
Information Sharing ◽  
Disaster Management ◽  
Support System ◽  
Disaster Response ◽  
Situational Awareness ◽  
Information Service ◽  
Information Platform ◽  
Disaster Information ◽  
Shared Information ◽  
The Cross

The common situational awareness among the disaster-response organizations and the appropriate action based on the information sharing are the key factor for the effective and efficient disaster response. Supported by the Cross-ministerial Strategic Innovation Promotion Program (SIP), we have developed the Shared Information Platform for Disaster Management (SIP4D) which facilitate the “cross-ministerial information sharing” by intermediating the various governmental organizations. Also, as the empirical research for utilize the shared disaster-information by SIP4D, we have developed the Medical Activity Support System for Disaster Management, the Reservoir Disaster Prevention Support System, and the Disaster Management Information Service Platform. In this paper, we introduce the overview of our R&D project, and report the implementation plans of our systems in the society.

Classifying Emergency Tweets for Disaster Response

2020 ◽  
Vol 3 (1) ◽  
pp. 14-29
Author(s):  
Rabindra Lamsal ◽  
T. V. Vijay Kumar
Keyword(s):  
Language Processing ◽  
Disaster Response ◽  
Situational Awareness ◽  
Primary Source ◽  
Fire Hazards ◽  
Community Needs ◽  
Crisis Events ◽  
Timely Access ◽  
Social Media Platforms ◽  
High Level

During disaster events such as floods, landslides, earthquakes, tsunamis, fire hazards, etc., social media platforms provide easy and timely access to information regarding the ongoing crisis events and thereby become an essential vehicle of information sharing. During such events, great amounts of such socially generated data becomes available, which can be accessed and processed to extract situational awareness insights. These insights, in turn, can be used to enhance the effectiveness and efficiency of disaster response in order to minimize the loss of lives and damage to property. People actively use social platforms like Facebook and Twitter to post information related to crisis events. Further, these platforms provide people the location and safety status of their family and friends during such events. Twitter, the microblogging platform, witnesses thousands of informally written tweets during crisis events, and since it provides high-level APIs to access its near real-time feed, it has become the primary source of data for researchers. It is generally observed that there is an exponential burst in the number of tweets during an ongoing crisis event. This sudden burst makes the task of monitoring, identifying, and processing each tweet virtually impossible for a human. However, such voluminous data can be processed using various machine learning and natural language processing techniques in coordination with a certain level of human interventions. This paper is focused on designing a semi-automated artificial intelligence-based classifier, which can classify the plethora of disaster-related tweets into various categories such as community needs, loss of lives, damage.

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