Access the full text.
Sign up today, get DeepDyve free for 14 days.
Zuopeng Zhao, Pei-yan Li, Xinzheng Xu (2013)
Forecasting Model of Coal Mine Water Inrush Based on Extreme Learning MachineApplied Mathematics & Information Sciences, 7
Martin Weickgenannt, Z. Kapelan, M. Blokker, D. Savić (2010)
Risk-Based Sensor Placement for Contaminant Detection in Water Distribution SystemsJournal of Water Resources Planning and Management, 136
Gemma Manache, C. Melching (2004)
Sensitivity Analysis of a Water-Quality Model using Latin Hypercube SamplingJournal of Water Resources Planning and Management, 130
K. Nguyen, R. Stewart, Hong Zhang (2013)
An intelligent pattern recognition model to automate the categorisation of residential water end-use eventsEnviron. Model. Softw., 47
R. Henderson, A. Baker, A. Baker, K. Murphy, K. Murphy, A. Hambly, R. Stuetz, Stuart Khan (2009)
Fluorescence as a potential monitoring tool for recycled water systems: a review.Water research, 43 4
A. Ostfeld, Elad Salomons (2004)
Optimal Layout of Early Warning Detection Stations for Water Distribution Systems SecurityJournal of Water Resources Planning and Management, 130
Shuming Liu, H. Che, Kate Smith, Lei Chen (2014)
Contamination event detection using multiple types of conventional water quality sensors in source water.Environmental science. Processes & impacts, 16 8
Junho Jeon, Joon Kim, B. Lee, S. Kim (2008)
Development of a new biomonitoring method to detect the abnormal activity of Daphnia magna using automated Grid Counter device.The Science of the total environment, 389 2-3
M. Sohn (2001)
Distance and cosine measures of niche overlapSoc. Networks, 23
P. Hawkins, Sreten Novic, P. Cox, B. Neilan, B. Burns, G. Shaw, W. Wickramasinghe, Y. Peerapornpisal, W. Ruangyuttikarn, T. Itayama, T. Saitou, M. Mizuochi, Y. Inamori (2005)
A review of analytical methods for assessing the public health risk from microcystin in the aquatic environmentJournal of Water Supply Research and Technology-aqua, 54
Corina Hoogh, A. Wagenvoort, Frank Jonker, J. Leerdam, A. Hogenboom (2006)
HPLC-DAD and Q-TOF MS techniques identify cause of Daphnia biomonitor alarms in the River Meuse.Environmental science & technology, 40 8
M. Tabacchi, C. Asensio, I. Pavón, M. Recuero, J. Mir, M. Artal (2013)
A statistical pattern recognition approach for the classification of cooking stages. The boiling water caseApplied Acoustics, 74
C. Marshall, S. Leuko, C. Coyle, M. Walter, B. Burns, B. Neilan (2007)
Carotenoid analysis of halophilic archaea by resonance Raman spectroscopy.Astrobiology, 7 4
Arlene Pascasio (2001)
An inequality on the cosines of a tight distance-regular graphLinear Algebra and its Applications, 325
M. Storey, Bram Gaag, Brendan Burns (2011)
Advances in on-line drinking water quality monitoring and early warning systems.Water research, 45 2
Y. Yang, R. Haught, J. Goodrich (2009)
Real-time contaminant detection and classification in a drinking water pipe using conventional water quality sensors: techniques and experimental results.Journal of environmental management, 90 8
N. Oliker, A. Ostfeld (2014)
A coupled classification - evolutionary optimization model for contamination event detection in water distribution systems.Water research, 51
A. Kessler, A. Ostfeld, G. Sinai (1998)
Detecting Accidental Contaminations in Municipal Water NetworksJournal of Water Resources Planning and Management, 124
Christina Stoecker, S. Welter, J. Moltz, Bianca Lassen, J. Kuhnigk, S. Krass, H. Peitgen (2013)
Determination of lung segments in computed tomography images using the Euclidean distance to the pulmonary artery.Medical physics, 40 9
Leo Liberti, C. Lavor, N. Maculan, A. Mucherino (2012)
Euclidean Distance Geometry and ApplicationsSIAM Rev., 56
Emergent contamination events have a significant impact on water systems. After contamination detection, it is important to classify the type of contaminant quickly to provide support for remediation attempts. Conventional methods generally either rely on laboratory-based analysis, which requires a long analysis time, or on multivariable-based geometry analysis and sequence analysis, which is prone to being affected by the contaminant concentration. This paper proposes a new contaminant classification method, which discriminates contaminants in a real time manner independent of the contaminant concentration. The proposed method quantifies the similarities or dissimilarities between sensors' responses to different types of contaminants. The performance of the proposed method was evaluated using data from contaminant injection experiments in a laboratory and compared with a Euclidean distance-based method. The robustness of the proposed method was evaluated using an uncertainty analysis. The results show that the proposed method performed better in identifying the type of contaminant than the Euclidean distance based method and that it could classify the type of contaminant in minutes without significantly compromising the correct classification rate (CCR).
Environmental Science: Processes & Impacts – Royal Society of Chemistry
Published: Dec 9, 2014
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.