Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches

Journal of Environmental Management
By: , and 



At public beaches, it is now common to mitigate the impact of water-borne pathogens by posting a swimmer's advisory when the concentration of fecal indicator bacteria (FIB) exceeds an action threshold. Since culturing the bacteria delays public notification when dangerous conditions exist, regression models are sometimes used to predict the FIB concentration based on readily-available environmental measurements. It is hard to know which environmental parameters are relevant to predicting FIB concentration, and the parameters are usually correlated, which can hurt the predictive power of a regression model. Here the method of partial least squares (PLS) is introduced to automate the regression modeling process. Model selection is reduced to the process of setting a tuning parameter to control the decision threshold that separates predicted exceedances of the standard from predicted non-exceedances. The method is validated by application to four Great Lakes beaches during the summer of 2010. Performance of the PLS models compares favorably to that of the existing state-of-the-art regression models at these four sites.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Partial least squares for efficient models of fecal indicator bacteria on Great Lakes beaches
Series title Journal of Environmental Management
DOI 10.1016/j.jenvman.2012.09.033
Volume 114
Year Published 2013
Language English
Publisher Elsevier
Contributing office(s) Wisconsin Water Science Center
Description 6 p.
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Journal of Environmental Management
First page 470
Last page 475
Country United States
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