Sunny with a chance of gastroenteritis: predicting swimmer risk at California beaches.
Environ Sci Technol
; 49(1): 423-31, 2015 Jan 06.
Article
in En
| MEDLINE
| ID: mdl-25489920
ABSTRACT
Traditional beach management that uses concentrations of cultivatable fecal indicator bacteria (FIB) may lead to delayed notification of unsafe swimming conditions. Predictive, nowcast models of beach water quality may help reduce beach management errors and enhance protection of public health. This study compares performances of five different types of statistical, data-driven predictive models multiple linear regression model, binary logistic regression model, partial least-squares regression model, artificial neural network, and classification tree, in predicting advisories due to FIB contamination at 25 beaches along the California coastline. Classification tree and the binary logistic regression model with threshold tuning are consistently the best performing model types for California beaches. Beaches with good performing models usually have a rainfall/flow related dominating factor affecting beach water quality, while beaches having a deteriorating water quality trend or low FIB exceedance rates are less likely to have a good performing model. This study identifies circumstances when predictive models are the most effective, and suggests that using predictive models for public notification of unsafe swimming conditions may improve public health protection at California beaches relative to current practices.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Bathing Beaches
/
Water Microbiology
/
Water Quality
/
Models, Statistical
Type of study:
Diagnostic_studies
/
Etiology_studies
/
Evaluation_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Country/Region as subject:
America do norte
Language:
En
Journal:
Environ Sci Technol
Year:
2015
Document type:
Article
Affiliation country:
United States