Crisis Management Systems: Big Data and Machine Learning Approach

Abderrazak Boumahdi, Mahmoud El Hamlaoui, Mahmoud Nassar

Abstract

A crisis is defined as an event that, by its nature or its consequences, poses a threat to the vital national interests or the basic needs of the population, encourages rapid decision making, and requires coordination between the various departments and agencies. Hence the need and importance of crisis and disaster management systems. These crisis and disaster management systems have several phases and techniques, and require many resources and tactics and needs. Among the needs of these systems are useful and necessary information that can be used to improve the making of good decisions, such as data on past and current crises. The application of machine learning and big data technologies in data processing of crises and disasters can yield important results in this area. In this document, we address in the first part the crisis management systems, and the tools of big data and machine learning that can be used. Then in the second part, we have established a literature review that includes a state of the art, and a discussion. Then we established a machine learning and big data approach for crisis management systems, with a description and experimentation, as well as a discussion of results and future work.

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Paper Citation


in Harvard Style

Boumahdi A., El Hamlaoui M. and Nassar M. (2020). Crisis Management Systems: Big Data and Machine Learning Approach.In Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-421-3, pages 603-610. DOI: 10.5220/0009790406030610


in Bibtex Style

@conference{enase20,
author={Abderrazak Boumahdi and Mahmoud El Hamlaoui and Mahmoud Nassar},
title={Crisis Management Systems: Big Data and Machine Learning Approach},
booktitle={Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2020},
pages={603-610},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009790406030610},
isbn={978-989-758-421-3},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Crisis Management Systems: Big Data and Machine Learning Approach
SN - 978-989-758-421-3
AU - Boumahdi A.
AU - El Hamlaoui M.
AU - Nassar M.
PY - 2020
SP - 603
EP - 610
DO - 10.5220/0009790406030610