Abstract
Research Article| October 31 2013 Optimal sensor placement for event detection and source identification in water distribution networks Shuming Liu; Shuming Liu 1School of Environment, Tsinghua University, Beijing 100084, China2Tsinghua University-VEOLIA Environment Joint Research Center for Advanced Environmental Technology, Tsinghua University, Beijing 100084, China E-mail: shumingliu@tsinghua.edu.cn Search for other works by this author on: This Site PubMed Google Scholar Pierre Auckenthaler Pierre Auckenthaler 1School of Environment, Tsinghua University, Beijing 100084, China Search for other works by this author on: This Site PubMed Google Scholar Journal of Water Supply: Research and Technology-Aqua (2014) 63 (1): 51–57. https://doi.org/10.2166/aqua.2013.106 Article history Received: June 05 2013 Accepted: September 19 2013 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Cite Icon Cite Permissions Search Site Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThis Journal Search Advanced Search Citation Shuming Liu, Pierre Auckenthaler; Optimal sensor placement for event detection and source identification in water distribution networks. Journal of Water Supply: Research and Technology-Aqua 1 February 2014; 63 (1): 51–57. doi: https://doi.org/10.2166/aqua.2013.106 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex This study focuses on the optimization of sensor placement with respect to the source identification and event detection. A multi-objective algorithm is used to solve the optimization problem. The numbers of possible source nodes for contamination events associated with the solutions on the Pareto fronts from the proposed method and benchmark method are calculated under the same configuration and compared. The comparison showed that the proposed method performs better than the benchmark method in detecting a contamination event and identifying its possible source. multi-objective genetic algorithm, nodal demand uncertainties, sensor placement, source identification This content is only available as a PDF. © IWA Publishing 2014 You do not currently have access to this content.
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