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Challenges and Opportunities of Using Big Data for Assessing Flood Risks
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
University of Chittagong, Bangladesh.ORCID iD: 0000-0002-7473-8185
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
2018 (English)In: Applications of Big Data Analytics: Trends, Issues, and Challenges / [ed] Mohammed M. Alani, Hissam Tawfik, Mohammed Saeed, Obinna Anya, Cham: Springer, 2018, p. 31-42Chapter in book (Refereed)
Abstract [en]

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has disastrous impact on the socioeconomic lifeline of a country. Nowadays, business organizations are using Big Data to improve their strategies and operations for revealing patterns and market trends to increase revenues. Eventually, the crisis response teams of a country have turned their interest to explore the potentialities of Big Data in managing disaster risks such as flooding. The reason for this is that during flooding, crisis response teams need to take decisions based on the huge amount of incomplete and inaccurate information, which are mainly coming from three major sources, including people, machines, and organizations. Hence, Big Data technologies can be used to monitor and to determine the people exposed to the risks of flooding in real time. This could be achieved by analyzing and processing sensor data streams coming from various sources as well as data collected from other sources such as Twitter, Facebook, and satellite and also from disaster organizations of a country by using Big Data technologies. Therefore, this chapter explores the challenges, the opportunities, and the methods, required to leverage the potentiality of Big Data to assess and predict the risk of flooding.

Place, publisher, year, edition, pages
Cham: Springer, 2018. p. 31-42
Keywords [en]
Big Data, Sensor streaming, Real time, Flooding, Risk assessment
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-70155DOI: 10.1007/978-3-319-76472-6ISBN: 978-3-319-76471-9 (print)ISBN: 978-3-319-76472-6 (electronic)OAI: oai:DiVA.org:ltu-70155DiVA, id: diva2:1234437
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251Available from: 2018-07-24 Created: 2018-07-24 Last updated: 2018-08-09Bibliographically approved

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Islam, Raihan UlAndersson, Karl

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