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A Web Based Belief Rule Based Expert System to Predict Flood
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0244-3561
University of Chittagong.
2015 (English)In: Proceedings of the 17th International Conference on Information Integration and Web-based Applications & Services (iiWAS2015) / [ed] Maria Indrawan-Santiago; Matthias Steinbauer; Ismail Khalil; Gabriele Anderst-Kotsis, New York: Association for Computing Machinery (ACM), 2015, 19-26 p., 3Conference paper, Published paper (Refereed)
Abstract [en]

Natural calamity disrupts our daily life and brings many sufferings in our life. Among the natural calamities, flood is one of the most catastrophic. Predicting flood helps us to take necessary precautions and save human lives. Several types of data (meteorological condition, topography, river characteristics, and human activities) are used to predict flood water level in an area. In our previous works, we proposed a belief rule based flood prediction system in a desktop environment. In this paper, we propose a web-service based flood prediction expert system by incorporating belief rule base with the capability of reading sensor data such as rainfall, river flow on real time basis. This will facilitate the monitoring of the various flood-intensifying factors, contributing in increasing the flood water level in an area. Eventually, the decision makers would able to take measures to control those factors and to reduce the intensity of flooding in an area.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2015. 19-26 p., 3
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing; Enabling ICT (AERI)
Identifiers
URN: urn:nbn:se:ltu:diva-27246DOI: 10.1145/2837185.2837212Local ID: 09c47bac-5b82-43aa-a855-c478efcdbf60ISBN: 978-1-4503-3491-4 (print)OAI: oai:DiVA.org:ltu-27246DiVA: diva2:1000429
Conference
International Conference on Information Integration and Web-based Applications & Services : 11/12/2015 - 13/12/2015
Projects
A belief-rule-based DSS to assess flood risks by using wireless sensor networks
Note
Godkänd; 2015; 20151014 (karand)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
In thesis
1. Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
Open this publication in new window or tab >>Wireless Sensor Network Based Flood Prediction Using Belief Rule Based Expert System
2017 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people.

This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed.

Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis.

Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed.

In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

Place, publisher, year, edition, pages
Luleå University of Technology, 2017. 112 p.
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
Keyword
WSN, Belief rule based Expert Systems, Flood prediction
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
urn:nbn:se:ltu:diva-66415 (URN)978-91-7790-004-7 (ISBN)978-91-7790-005-4 (ISBN)
Presentation
2017-12-12, A193 Campus Skellefteå, Forskargatan 1, 931 62, Skellefteå, 08:00 (English)
Opponent
Supervisors
Available from: 2017-11-08 Created: 2017-11-07 Last updated: 2017-11-27Bibliographically approved

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