Predicting recreational water quality advisories: A comparison of statistical methods. (February 2016)

• Record Type: Journal Article
• Authors: Brooks, Wesley; Corsi, Steven; Fienen, Michael; Carvin, Rebecca
• Is Part Of: Environmental modelling & software. Volume 76(2016:Feb.)
• Page Start: 81
• Page End: 94
• Publication Date: 2016-02
• Subjects: Regression model -- Performance evaluation -- Beach health -- Water quality; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2015.10.012
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Highlights: Fourteen regression methods were compared for water-quality prediction. A random forest method produced the most accurate predictions of all methods. Adaptive LASSO produced the most accurate predictions of the linear model options.