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An Intelligent Flood Risk Assessment System using Belief Rule Base
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Natural disasters disrupt our daily life and cause many sufferings. Among the various natural disasters, flood is one of the most catastrophic. Assessing flood risk helps to take necessary precautions and can save human lives. The assessment of risk involves various factors which can not be measured with hundred percent certainty. Therefore, the present methods of flood risk assessment can not assess the risk of flooding accurately. 

This research rigorously investigates various types of uncertainties associated with the flood risk factors. In addition, a comprehensive study of the present flood risk assessment approaches has been conducted. Belief Rule Base expert systems are widely used to handle various of types of uncertainties. Therefore, this research considers BRBES’s approach to develop an expert system to assess the risk of flooding. In addition, to facilitate the learning procedures of BRBES, an optimal learning algorithm has been proposed. The developed BRBES has been applied taking real world case study area, located at Cox’s Bazar, Bangladesh. The training data has been collected from the case study area to obtain the trained BRB and to develop the optimal learning model. The BRBES can generate different "What-If" scenarios which enables the analysis of flood risk of an area from various perspectives which makes the system robust and sustainable. This system is said to be intelligent as it has knowledge base, inference engine as well as the learning capability.

Place, publisher, year, edition, pages
2017. , p. 96
Keywords [en]
Belief Rule Base, flood risk assessment, uncertainty, expert systems, optimization, RESTful API
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:ltu:diva-65390OAI: oai:DiVA.org:ltu-65390DiVA, id: diva2:1136645
Educational program
Computer Science and Engineering, master's level (120 credits)
Supervisors
Examiners
Available from: 2017-08-31 Created: 2017-08-28 Last updated: 2017-08-31Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf