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A Neural Network-Based Situational Awareness Approach for Emergency Response

Mehdi Ben Lazreg has submitted his thesis entitled “A Neural Network-Based Situational Awareness Approach for Emergency Response” and will defend it for det assessment committee Tuesday 28 April 2020.

This thesis studies social media analysis platforms and the reason behind the emergency management services reluctance.

Mehdi Ben Lazreg

PhD Candidate

The disputation will be held digitally, because of the Corona covid-19-situation. Spectators may follow the disputation digitally – link and thesis is available below.

Mehdi Ben Lazreg at the Faculty of Engineering and Science has submitted his thesis entitled “A Neural Network-Based Situational Awareness Approach for Emergency Response”, and will defend the thesis for the PhD-degree Tuesday 28 April 2020.

He has followed the PhD programme at the Faculty of Engineering and Science with specialisation in ICT.

Summary of the Thesis by Mehdi Ben Lazreg:

A Neural Network-Based Situational Awareness Approach for Emergency Response

In recent years, social media has become a part of daily life for many of us, and increasingly an open medium for emergency communications.

Emergency management services use social media to inform the public about the status of an emergency and what precaution the public needs to take. Also, the citizens use these online mediums to check on the safety of loved ones. Finally, eyewitness and affected individuals share their observations, concerns, and challenges they face during an emergency.

That information can potentially benefit emergency management services to improve situational awareness. However, emergency management services are still reluctant to use social media as a source of information.

Social media challenges

This thesis studies social media analysis platforms and the reason behind the emergency management services reluctance.

We identify two main challenges:

  • The first challenge is the language used on social media, which includes misspellings, leetspeak, and abbreviations. This myriad of language usages requires a specific and adaptive normalization technique.
  • The second challenge is that most of the dominant platforms try to find accurate ways of extracting as much information related to the crisis as possible. Consequently, they do not explicitly address the specific information-requirements of the time-constrained emergency personnel. This vast amount of supplied information causes an overflow of information, and in many aspects, renders the analytics platforms less useful.

Proposals to solve the challenges

As a mitigation to the first challenge, we propose a string metric that embraces similarities between text strings based on both the character similarities between the words and the context of these words.

For the second challenge, we propose an intelligent information retrieval framework for social media that, given the status of the emergency, provides the information most likely needed by the emergency management services.

The analytics framework we introduce combines two main components.

  • The first component classifies social media messages into separate topics representing information required by emergency services during a specific situation.
  • The second component decides which information to retrieve by learning what the emergency services need, based on the information available and the status of the emergency.

Disputation facts:

The CandidateMehdi Ben Lazreg (1987, Sousse, Tunisia), BA High school of communication of Tunis, Tunisia (2011), MA - Master Grade Thesis: A Churn prediction model based on gaussian processes. University of Agder (2013).

The trial lecture and the public defence will take place in internet (link below), Tuesday 28 April 2020.

Professor Andreas Prinz, Department of ICT, UiA, will chair the disputation.

Trial lecture at 10:15

Public defense at 12:15

Given topic for trial lecture: "User-centered design of information systems"

Thesis Title: “A Neural Network-Based Situational Awareness Approach for Emergency Response”

Search for the thesis in AURA - Agder University Research Archive, a digital archive of scientific papers, theses and dissertations from the academic staff and students at the University of Agder.


First opponent: Dr. Muhammad Imran, Hamad bin Khalifa University, Doha, Qatar

Second opponent: Professor Jim Tørresen, University of Oslo, Norway

Professor Frank Reichert, Department of ICT, UiA, is appointed as the administrator for the assessment commitee.

Supervisors were Associate Professor Morten Goodwin, Department of ICT, UiA (main supervisor) and Professor Ole-Christoffer Granmo, Department of ICT, UiA (co-supervisor)

What to do as an audience member:

We ask audience members to join the virtual trial lecture at 10:05 at the earliest and the public defense at 12:05 at the earliest. After these times, you can leave and rejoin the meeting at any time. Further, we ask audience members to turn off their microphone and camera and keep them turned off throughout the event. You do this at the bottom left of the image when in Zoom. We recommend you use ‘Speaker view’. You select that at the top right corner of the video window when in Zoom.

Opponent ex auditorio: The chair invites members of the public to pose questions ex auditorio in the introduction to the public defense, with deadlines. Questions can be submitted to the chair Andreas Prinz at e-mail andreas.prinz@uia.no.

The thesis is available here:

 PhD Thesis - Mehdi Ben Lazreg