Mounce, S.R., mounce, R.B. and Boxall, J.B. (2015) Case-based reasoning to support decision making for managing drinking water quality events in distribution systems. Urban Water Journal. Published online 21 May 2015. ISSN 1744-9006
Abstract
In order to better leverage past experience of water quality incidents, and to tap into the unique incident database currently being maintained and required by regulatory authorities, a data mining approach is herein proposed. The quality of drinking water is paramount to protecting public health. However water quality failures do occur, with some of the hardest to understand and manage occurring within distribution systems. In the UK, a regulatory process is applied in which water service providers must report on significant water quality incidents, their causes, actions and outcomes. These reports form a valuable resource that can be explored for improved understanding, to help with future incident management and evaluate potential solutions. Case-based reasoning is a knowledge-based problem-solving technique that relies on the reuse of past experience. The WaterQualityCBR software system presented here was developed as such a decision support tool to more effectively manage water quality in distribution systems.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 Taylor & Francis. This is an author produced version of a paper subsequently published in Urban Water Journal. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Civil and Structural Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Jun 2015 15:58 |
Last Modified: | 22 May 2016 12:51 |
Published Version: | http://dx.doi.org/10.1080/1573062X.2015.1036082 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/1573062X.2015.1036082 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:86843 |