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Development of Multiple Regression Models to Predict Sources of Fecal Pollution
Author(s) -
Hall Kimberlee K.,
Scheuerman Phillip R.
Publication year - 2017
Publication title -
water environment research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 73
eISSN - 1554-7531
pISSN - 1061-4303
DOI - 10.2175/106143017x14839994523901
Subject(s) - watershed , multivariate statistics , environmental science , water quality , pollution , regression analysis , hydrology (agriculture) , fecal coliform , statistics , stepwise regression , regression , linear discriminant analysis , streams , mathematics , computer science , engineering , ecology , machine learning , geotechnical engineering , biology , computer network
This study assessed the usefulness of multivariate statistical tools to characterize watershed dynamics and prioritize streams for remediation. Three multiple regression models were developed using water quality data collected from Sinking Creek in the Watauga River watershed in Northeast Tennessee. Model 1 included all water quality parameters, model 2 included parameters identified by stepwise regression, and model 3 was developed using canonical discriminant analysis. Models were evaluated in seven creeks to determine if they correctly classified land use and level of fecal pollution. At the watershed level, the models were statistically significant ( p < 0.001) but with low r 2 values (Model 1 r 2 = 0.02, Model 2 r 2 = 0.01, Model 3 r 2 = 0.35). Model 3 correctly classified land use in five of seven creeks. These results suggest this approach can be used to set priorities and identify pollution sources, but may be limited when applied across entire watersheds.