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RULE-BASED DECISION SUPPORT SYSTEM FOR SENSOR DEPLOYMENT IN DRINKING WATER NETWORKS

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Date Issued:
2011
Abstract/Description:
Drinking water distribution systems are inherently vulnerable to malicious contaminant events with environmental health concerns such as total trihalomethanes (TTHMs), lead, and chlorine residual. In response to the needs for long-term monitoring, one of the most significant challenges currently facing the water industry is to investigate the sensor placement strategies with modern concepts of and approaches to risk management. This study develops a Rule-based Decision Support System (RBDSS) to generate sensor deployment strategies with no computational burden as we oftentimes encountered via large-scale optimization analyses. Three rules were derived to address the efficacy and efficiency characteristics and they include: 1) intensity, 2) accessibility, and 3) complexity rules. To retrieve the information of population exposure, the well-calibrated EPANET model was applied for the purpose of demonstration of vulnerability assessment. Graph theory was applied to retrieve the implication of complexity rule eliminating the need to deal with temporal variability. In case study 1, implementation potential was assessed by using a small-scale drinking water network in rural Kentucky, the United States with the sensitivity analysis. The RBDSS was also applied to two networks, a small-scale and large-scale network, in "The Battle of the Water Sensor Network" (BWSN) in order to compare its performances with the other models. In case study 2, the RBDSS has been modified by implementing four objective indexes, the expected time of detection (Z1), the expected population affected prior to detection (Z2), the expected consumption of contaminant water prior to detection, and the detection likelihood (Z4), are being used to evaluate RBDSS's performance and compare to other models in Network 1 analysis in BWSN. Lastly, the implementation of weighted optimization is applied to the large water distribution analysis in case study 3, Network 2 in BWSN.
Title: RULE-BASED DECISION SUPPORT SYSTEM FOR SENSOR DEPLOYMENT IN DRINKING WATER NETWORKS.
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Name(s): Prapinpongsanone, Natthaphon, Author
Chang, Ni-Bin, Committee Chair
University of Central Florida, Degree Grantor
Type of Resource: text
Date Issued: 2011
Publisher: University of Central Florida
Language(s): English
Abstract/Description: Drinking water distribution systems are inherently vulnerable to malicious contaminant events with environmental health concerns such as total trihalomethanes (TTHMs), lead, and chlorine residual. In response to the needs for long-term monitoring, one of the most significant challenges currently facing the water industry is to investigate the sensor placement strategies with modern concepts of and approaches to risk management. This study develops a Rule-based Decision Support System (RBDSS) to generate sensor deployment strategies with no computational burden as we oftentimes encountered via large-scale optimization analyses. Three rules were derived to address the efficacy and efficiency characteristics and they include: 1) intensity, 2) accessibility, and 3) complexity rules. To retrieve the information of population exposure, the well-calibrated EPANET model was applied for the purpose of demonstration of vulnerability assessment. Graph theory was applied to retrieve the implication of complexity rule eliminating the need to deal with temporal variability. In case study 1, implementation potential was assessed by using a small-scale drinking water network in rural Kentucky, the United States with the sensitivity analysis. The RBDSS was also applied to two networks, a small-scale and large-scale network, in "The Battle of the Water Sensor Network" (BWSN) in order to compare its performances with the other models. In case study 2, the RBDSS has been modified by implementing four objective indexes, the expected time of detection (Z1), the expected population affected prior to detection (Z2), the expected consumption of contaminant water prior to detection, and the detection likelihood (Z4), are being used to evaluate RBDSS's performance and compare to other models in Network 1 analysis in BWSN. Lastly, the implementation of weighted optimization is applied to the large water distribution analysis in case study 3, Network 2 in BWSN.
Identifier: CFE0003704 (IID), ucf:48825 (fedora)
Note(s): 2011-05-01
M.S.Env.E.
Engineering and Computer Science, Department of Civil and Environmental Engineering
Masters
This record was generated from author submitted information.
Subject(s): rule-based decision support system
sensor deployment
water distribution network
Persistent Link to This Record: http://purl.flvc.org/ucf/fd/CFE0003704
Restrictions on Access: public
Host Institution: UCF

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