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Predicting recreational water quality advisories: A comparison of statistical methods

Environmental Modelling and Software

Great Lakes Restoration Initiative and Ocean Research Priority Plan
By:
, , ORCID iD , and
https://doi.org/10.1016/j.envsoft.2015.10.012

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Abstract

Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

Additional publication details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Predicting recreational water quality advisories: A comparison of statistical methods
Series title:
Environmental Modelling and Software
DOI:
10.1016/j.envsoft.2015.10.012
Volume:
76
Year Published:
2016
Language:
English
Publisher:
Elsevier
Contributing office(s):
Wisconsin Water Science Center
Description:
14 p.
First page:
81
Last page:
94
Online Only (Y/N):
N
Additional Online Files (Y/N):
Y