AbstractHigh levels of fecal-indicator organisms (FIOs) at bathing water sites can cause disease and impose threats to public health. There is a need for predicting FIO levels to inform the public and reduce exposure. Data-driven models are one of the main tools being considered as predictive models. However, identifying the main inputs of the data-driven models is a major challenge in developing FIO predictor models. This paper develops a data-driven model for FIO concentration prediction based on a limited number of critical input variables. Essential variables were identified with be a combination of the gamma test and Genetic Algorithm (Gamma-GA test). Artificial neural networks (ANNs) and linear regression models were developed using these two variable identification approaches for comparison. The models were applied to a case study, and it was found that the model using the Gamma-GA test has a high potential to predict FIO levels more accurately, although this requires further investigation with different case studies. A correlation analysis was required prior to the variable identification approaches in this study. The need of this analysis highlights the significance of understanding the waterbody and the data set in the development and application of data-driven models. Models using a Gamma-GA test were more capable of predicting extreme (high) FIO concentrations, making a Gamma-GA test more suitable for a bathing water quality early warning system. The importance of nonlinearity in such predictive models was also demonstrated by the better performance of nonlinear ANN models compared with linear regression models regardless of the variable identification approaches used. This paper highlights the importance of nonlinearity in bathing water quality prediction and encourages further utilization of nonlinear models for this application.

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