Microbial contamination in beach water poses a public health threat due to waterborne diseases. To reduce the risk of exposure to fecal contamination, informing beachgoers in advance about the microbial water quality is important. Currently, determining the level of fecal contamination takes 24 h. The objective of this study is to predict the current level of fecal contamination (enterococcus [ENT] and Escherichia coli) using readily available environmental variables. Artificial neural network (ANN) and support vector regression (SVR) models were constructed using data from the Haeundae and Gwangalli Beaches in Busan City. The input variables included the tidal level, air and water temperature, solar radiation, wind direction and velocity, precipitation, discharge from the wastewater treatment plant, and suspended solid concentration in beach water. The dependence of fecal contamination on the input variables was statistically evaluated; precipitation, discharge from the wastewater treatment plant, and wind direction at the two beaches were positively correlated to the changes in the two bacterial concentrations (p < 0.01), whereas solar radiation was negatively correlated (p < 0.01). The performance of the ANN model for predicting ENT and E. coli at Gwangalli Beach was significantly higher than that of the SVR model with the training dataset (p < 0.05). Based on the comparison of residual values between the predicted and observed fecal indicator bacteria concentrations in two models, the ANN demonstrated better performance than SVR. This study suggests an effective prediction method to determine whether a beach is safe for recreational use. Core Ideas Enterococcus and E. coli concentrations were predicted using machine learning models. Nine variables collected from two beach waters were tested as input for the models. The ANN performed better than SVR for predicting fecal indicator bacteria concentrations.

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We analyzed the occurrence of enteric viruses and bacteria at 22 places of drinkable groundwater (civil defense emergency water-supply facility), 8 places of the groundwater used for drinking water in group food services, and 10 places of spring-water. When the 40 concentrated samples were analyzed using nested RT-PCR and real-time RT PCR methods, norovirus and other enteric viruses were not detected in all samples tested. The detection percentages for total coliforms, Escherichia coli, Yersinia enterocolitica of fecal indicator were 57.5%, 22.5% and 7.5%, respectively. Colipages were not detected. These results suggest that high levels of fecal indicator bacteria in groundwater and spring-water are not directly related to occurrence of enteric viruses.