Predicting Culturable Enterococci Exceedances at Escambron Beach, San Juan, Puerto Rico using Satellite Remote Sensing and Artificial Neural Networks
bathing water quality, enterococci, machine learning, remote sensing, water quality prediction
Digital Object Identifier (DOI)
Predicting recreational water quality is key to protecting public health from exposure to wastewater-associated pathogens. It is not feasible to monitor recreational waters for all pathogens; therefore, monitoring programs use fecal indicator bacteria (FIB), such as enterococci, to identify wastewater pollution. Artificial neural networks (ANNs) were used to predict when culturable enterococci concentrations exceeded the U.S. Environmental Protection Agency (U.S. EPA) Recreational Water Quality Criteria (RWQC) at Escambron Beach, San Juan, Puerto Rico. Ten years of culturable enterococci data were analyzed together with satellite-derived sea surface temperature (SST), direct normal irradiance (DNI), turbidity, and dew point, along with local observations of precipitation and mean sea level (MSL). The factors identified as the most relevant for enterococci exceedance predictions based on the U.S. EPA RWQC were DNI, turbidity, cumulative 48 h precipitation, MSL, and SST; they predicted culturable enterococci exceedances with an accuracy of 75% and power greater than 60% based on the Receiving Operating Characteristic curve and F-Measure metrics. Results show the applicability of satellite-derived data and ANNs to predict recreational water quality at Escambron Beach. Future work should incorporate local sanitary survey data to predict risky recreational water conditions and protect human health.
Citation / Publisher Attribution
Journal of Water & Health, v. 17, issue 1, p. 137-148
Scholar Commons Citation
Laureano-Rosario, Abdiel E.; Duncan, Andrew P.; Symonds, Erin Michelle; Savic, Dragan A.; and Muller-Karger, Frank, "Predicting Culturable Enterococci Exceedances at Escambron Beach, San Juan, Puerto Rico using Satellite Remote Sensing and Artificial Neural Networks" (2019). Marine Science Faculty Publications. 629.