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A Belief Rule Based Flood Risk Assessment Expert System using Real Time Sensor Data Streaming
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: Proveedings of the 43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Piscataway, NJ: IEEE Computer Society, 2018, p. 8-45Conference paper, Published paper (Refereed)
Abstract [en]

Among the various natural calamities, flood is considered one of the most catastrophic natural hazards, which has a significant impact on the socio-economic lifeline of a country. The Assessment of flood risks facilitates taking appropriate measures to reduce the consequences of flooding. The flood risk assessment requires Big data which are coming from different sources, such as sensors, social media, and organizations. However, these data sources contain various types of uncertainties because of the presence of incomplete and inaccurate information. This paper presents a Belief rule-based expert system (BRBES) which is developed in Big data platform to assess flood risk in real time. The system processes extremely large dataset by integrating BRBES with Apache Spark while a web-based interface has developed allowing the visualization of flood risk in real time. Since the integrated BRBES employs knowledge driven learning mechanism, it has been compared with other data-driven learning mechanisms to determine the reliability in assessing flood risk. The integrated BRBES produces reliable results in comparison to other data-driven approaches. Data for the expert system has been collected by considering different case study areas of Bangladesh to validate the system.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2018. p. 8-45
Keywords [en]
Belief Rule Base, Flood risk assessment, Uncertainty, Expert systems, Sensor data streaming, Big data
National Category
Computer Sciences Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-70318DOI: 10.1109/LCNW.2018.8628607ISI: 000461284400006Scopus ID: 2-s2.0-85062868652OAI: oai:DiVA.org:ltu-70318DiVA, id: diva2:1237758
Conference
43nd IEEE Conference on Local Computer Networks Workshops (LCN Workshops), Chicago, October 1-4, 2018
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Funder
Swedish Research Council, 2014-4251Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2019-07-11Bibliographically approved

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Islam, Raihan UlAndersson, Karl

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CiteExportLink to record
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