LAUSR

Abstract Fecal coliform bacteria are important indicator microorganisms that are commonly monitored monthly for the quality of oyster harvest waters and the end product, making the protection of public health challenging as oyster harvest may occur daily and fecal coliform levels in oyster harvest waters may also change daily. This paper presents an artificial intelligence-based neural network modeling approach to predict fecal coliform bacteria levels in oyster harvest areas (waters) daily. The new approach was demonstrated by developing an artificial neural network (ANN) model for daily prediction of fecal coliform levels in seven oyster harvest areas along the Northern Gulf of Mexico coast. The model input variables were selected by using the stepwise regression analysis method. It was found that the prevalence of fecal coliform bacteria in oyster growing waters was controlled by six independent environmental predictors, including wind, salinity, tide, water temperature, rainfall, and solar radiation, which were utilized as the model input variables. It was also found that the prevalence of fecal coliform bacteria in oyster growing waters is affected by not only current conditions of the six independent environmental variables but also antecedent conditions of the variables (particularly average solar radiation and cumulative rainfall over the past two days). Model prediction results indicated that the ANN model was capable to predict not only daily variations in fecal coliform levels but also seasonal fluctuations in observed fecal coliform levels characterized by high bacteria levels in the cold season and low bacteria levels in the warm season. The performance of the ANN model was demonstrated by the linear correlation coefficient (LCC) of 0.7421 and root mean square (RMSE) of 0.3844 for the model development phase and the LCC of 0.6312 and RMSE of 0.2835 for the independent validation phase. The ANN model makes it possible to reduce the harvest and consumption of fecally contaminated oysters and thereby greatly reduce the health risk to the general public and particularly oyster consumers. Although the predictive ANN model was specifically developed for oyster harvest areas along the Louisiana Gulf coast, the methods used in this paper are generally applicable to other oyster harvest areas and coastal waters.