Contaminant Spread Forecasting and Confirmatory Sampling Location Identification in a Water-Distribution System
Publication: Journal of Water Resources Planning and Management
Volume 142, Issue 12
Abstract
While significant emphasis has been placed on contamination warning system design, event detection, and source identification, relatively little emphasis has been placed on characterizing contaminant spread and confirmatory sampling. This study developed algorithms that utilize contamination source probabilities to forecast contaminant spread, which is then used to identify confirmatory sampling locations to maximize contaminant spread information based on entropy concepts. The algorithms were applied to simulated contamination scenarios using one small network with five-sensor locations, and one large network with 5-, 10-, 20-, or 50-sensor locations. The first step in the forecasting process was to identify the past contamination probability using existing sensor information and a probabilistic contamination source identification algorithm. In general, the past contamination status of the nodes was either correctly identified or did not have enough information to classify; incorrect classifications were typically less than 3%. The past contamination status was then used to forecast contaminant spread 4 h into the future with the classification performance for the correct (32–53%) and incorrect (6–17%) identification, and unable to classify (30–57%) categories directly dependent on the accuracy of past contamination characterization. In general, forecasting accuracy increased with the number of sensor locations and decreased with longer forecasting time horizons. Confirmatory sampling locations were identified using the forecasted spread information and compared with locations selected using actual (i.e., perfectly known) spread for comparison. Both the forecasted and actual approaches generated similar confirmatory sampling locations. In general, the confirmatory sampling locations identified with the estimated forecasts created new information (i.e., reduced the number of nodes with unknown status), while using the actual forecasts reinforced existing information. As a result of the new information generated, the confirmatory sampling locations identified with the estimated forecasts tended to better characterize the contamination event than the locations identified with the actual sensor information known up to the future time of sampling.
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Acknowledgments
This work was completed as part of S. M. M. Rana’s Master’s thesis at the University of Cincinnati. This research was partially funded by the United States Geological Survey through the Ohio Water Resource Research Institute. The writers thank Robert Janke of the U.S. EPA and James Uber of the University of Cincinnati for their valuable discussion and opinion on the subject matter. The views expressed in this paper are those of the writers and do not necessarily reflect those of the funding agencies. Mention of trade names or commercial products does not constitute endorsement or recommendations for use.
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Published In
Journal of Water Resources Planning and Management
Volume 142 • Issue 12 • December 2016
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Sep 7, 2015
Accepted: May 26, 2016
Published online: Jul 22, 2016
Published in print: Dec 1, 2016
Discussion open until: Dec 22, 2016
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