Every reference with a DOI in the deposited reference list resolved to a known
work in Crossref or DataCite at the dated check, and none carried a retraction,
withdrawal, or removal notice.
The 65 checked references that resolve
resolves10.1186/s12940-017-0347-9Estimate of incidence and cost of recreational waterborne illness on United States surface waters
resolves10.1093/aje/kwx019Acute Illness Among Surfers After Exposure to Seawater in Dry- and Wet-Weather Conditions
resolves10.1016/j.watres.2012.01.033Using rapid indicators for Enterococcus to assess the risk of illness after exposure to urban runoff contaminated marine water
resolves10.1289/ehp.6241Do U.S. Environmental Protection Agency water quality guidelines for recreational waters prevent gastrointestinal illness? A systematic review and meta-analysis.
resolves10.1016/j.watres.2014.03.050Effect of submarine groundwater discharge on bacterial indicators and swimmer health at Avalon Beach, CA, USA
resolves10.1007/s12403-009-0012-9Skin-related symptoms following exposure to recreational water: a systematic review and meta-analysis
resolves10.1021/acs.est.5b04372Human and Bovine Viruses and Bacteria at Three Great Lakes Beaches: Environmental Variable Associations and Health Risk
resolves10.1021/es020524uDecadal and Shorter Period Variability of Surf Zone Water Quality at Huntington Beach, California
resolves10.1016/j.wroa.2018.10.003Within-day variability in microbial concentrations at a UK designated bathing water: Implications for regulatory monitoring and the application of predictive modelling based on historical compliance data
resolves10.1021/es034382vPublic Mis-Notification of Coastal Water Quality: A Probabilistic Evaluation of Posting Errors at Huntington Beach, California
resolves10.1029/2006WR005142Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space
resolves10.3390/w11071387Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting
resolves10.3390/w10111536Flood Prediction Using Machine Learning Models: Literature Review
resolves10.3390/w12010096Convolutional Neural Network Coupled with a Transfer-Learning Approach for Time-Series Flood Predictions
resolves10.4319/lo.2008.53.2.0487Predicting marine phytoplankton maximum growth rates from temperature: Improving on the Eppley curve using quantile regression
resolves10.2166/hydro.2017.010Application of the Random Forest model for chlorophyll-a forecasts in fresh and brackish water bodies in Japan, using multivariate long-term databases
resolves10.1111/1365-2664.12820WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current
resolves10.1016/j.jhydrol.2011.05.024Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis
resolves10.1021/es703185pNowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study
resolves10.1021/es504701jSunny with a Chance of Gastroenteritis: Predicting Swimmer Risk at California Beaches
resolves10.1016/j.mimet.2020.105970Nowcasting methods for determining microbiological water quality at recreational beaches and drinking-water source waters
resolves10.1016/s0043-1354(01)00123-3Relationships between microbial water quality and environmental conditions in coastal recreational waters: the fylde coast, UK
resolves10.1016/j.watres.2014.09.001Predicting water quality at Santa Monica Beach: Evaluation of five different models for public notification of unsafe swimming conditions
resolves10.1021/acs.est.5b05378Comparative Evaluation of Statistical and Mechanistic Models of <i>Escherichia coli</i> at Beaches in Southern Lake Michigan
resolves10.1021/es0515250Enterococci Predictions from Partial Least Squares Regression Models in Conjunction with a Single-Sample Standard Improve the Efficacy of Beach Management Advisories
resolves10.1016/j.watres.2005.09.031Statistical basis for predicting the need for bacterially induced beach closures: Emergence of a paradigm?
resolves10.1007/s10661-012-2716-8Hydrometeorological variables predict fecal indicator bacteria densities in freshwater: data-driven methods for variable selection
resolves10.2134/jeq2017.11.0425Development of a Nowcasting System Using Machine Learning Approaches to Predict Fecal Contamination Levels at Recreational Beaches in Korea
resolves10.1016/j.watres.2020.115788A predictive model of recreational water quality based on adaptive synthetic sampling algorithms and machine learning
resolves10.1021/acs.est.8b01022Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach
resolves10.3133/fs20193061Real-time assessments of water quality—A nowcast for <i>Escherichia coli</i> and cyanobacterial toxins
resolves10.1016/j.watres.2010.12.010Efficacy of monitoring and empirical predictive modeling at improving public health protection at Chicago beaches
resolves10.1021/es402303wA Coupled Modeling and Molecular Biology Approach to Microbial Source Tracking at Cowell Beach, Santa Cruz, CA, United States
resolves10.1021/es062822nBeach Sands along the California Coast Are Diffuse Sources of Fecal Bacteria to Coastal Waters
resolves10.1021/es071807vEnterococci Concentrations in Diverse Coastal Environments Exhibit Extreme Variability
resolves10.1029/2003GL019122Covariation of coastal water temperature and microbial pollution at interannual to tidal periods
resolves10.1007/s10651-007-0043-yComponents of information for multiple resolution comparison between maps that share a real variable
resolves10.1039/C7EM00594FFrequent detection of a human fecal indicator in the urban ocean: environmental drivers and covariation with enterococci
resolves10.1016/j.jhydrol.2010.06.033Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds
resolves10.1016/j.watres.2012.12.030Differentiating Enterococcus concentration spatial, temporal, and analytical variability in recreational waters
resolves10.1021/es034978i<i>Escherichia coli</i> Sampling Reliability at a Frequently Closed Chicago Beach: Monitoring and Management Implications
resolves10.1021/es9015124Covariation and Photoinactivation of Traditional and Novel Indicator Organisms and Human Viruses at a Sewage-Impacted Marine Beach
The 18 references without a DOI — listed, not checked
no DOI — not checkedUSEPA. BEACON—Beach Advisory and Closing On-line
Notification. https://ofmpub.epa.gov/apex/beacon2/f?p=137:8:NO: (accessed Jan 25, 2018).
no DOI — not checkedAssembly Bill 411: Beach Sanitation: Posting
no DOI — not checkedRecreational Water Quality Criteria
no DOI — not checkedHeal the Bay. Heal the Bay 2017–2018 Beach Report Card, 2018.
no DOI — not checkedMethod 1600: Enterococci in Water by Membrane Filtration Using Membrane-Enterococcus Indoxyl-Beta-D-Glucoside Agar (MEI). EPA 821-R-06-009
no DOI — not checkedMethod 1603: Escherichia Coli in Water by Membrane Filtration Using Modified Membrane-Tolerant Escherichia Coli Agar (Modified m-TEC)
no DOI — not checkedStatistical Framework for Water Quality Criteria and Monitoring
no DOI — not checkedDirective 2006/7/EC of the European Parliament and of the Council of 15 February 2006 Concerning the Management of Bathing Water Quality and Repealing Directive 76/160/EEC
no DOI — not checkedGuidelines for Safe Recreational Water Environments
no DOI — not checkedDeveloping and Implementing Predictive Models for Estimating Recreational Water Quality at Great Lakes Beaches
no DOI — not checkedTuckey, B.; Brown, N.; Neale, M.; Chakravarthy, K. Safeswim—Live Information System for Water Quality and Swimming Conditions at Bathing Beaches. Australasian Coasts and Ports 2019 Conference: Future directions from 40 [degrees] S and beyond, Hobart, 10–13 September 2019, 2019; p 1165.
no DOI — not checkedUN
Environment Programme. Progress
on Ambient Water Quality:
Piloting the monitoring methodology and initial findings for SDG indicator
6.3.2. https://www.unenvironment.org/resources/report/progress-ambient-water-quality-piloting-monitoring-methodology-and-initial-2 (accessed Sept 7, 2020).
no DOI — not checkedCalifornia Environmental Data Exchange Network (CEDEN). http://ceden.org/san_diego_swamp.shtml (accessed Sept 7, 2020).
no DOI — not checkedSearcy, R. T.; Boehm, A. B. Data for “A day at the beach: Enabling coastal water quality prediction with high-frequency sampling and data-driven models”. https://purl.stanford.edu/vh736vq8124 (accessed Sept 8, 2020).
no DOI — not checkedref71/cit71
no DOI — not checkedNOAA
Fisheries. Southwest Fisheries
Science Center. Environmental Research Division. https://oceanview.pfeg.noaa.gov/products/upwelling/intro (accessed
Sept 7, 2020).
no DOI — not checkedscikit-learn: machine
learning in Python—scikit-learn 0.23.2
documentation. https://scikit-learn.org/stable/ (accessed Sept 7, 2020).
no DOI — not checkedBlue Water Task
Force. https://bwtf.surfrider.org/ (accessed Sept 7, 2020).
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