Appraisal of machine learning techniques for predicting emerging disinfection byproducts in small water distribution networks

Monitoring emerging disinfection byproducts (DBPs) is challenging for many small water distribution networks (SWDNs), and machine learning-based predictive modeling could be an alternative solution. In this study, eleven machine learning techniques, including three multivariate linear regression-based, three regression tree-based, three neural networks-based, and two advanced non-parametric regression techniques, are used to develop models for predicting three emerging DBPs (dichloroacetonitrile, chloropicrin, and trichloropropanone) in SWDNs. Predictors of the models include commonly-measured water quality parameters and two conventional DBP groups. Sampling data of 141 cases were collected from eleven SWDNs in Canada, in which 70 % were randomly selected for model training and the rest were used for validation. The modeling process was reiterated 1000 times for each model. The results show that models developed using advanced regression techniques, including support vector regression and Gaussian process regression, exhibited the best prediction performance. Support vector regression models showed the highest prediction accuracy (R2 = 0.94) and stability for predicting dichloroacetonitrile and trichloropropanone, and Gaussian process regression models are optimal for predicting chloropicrin (R2 = 0.92). The difference is likely due to the much lower concentrations of chloropicrin than dichloroacetonitrile and trichloropropanone. Advanced non-parametric regression techniques, characterized by a probabilistic nature, were identified as most suitable for developing the predictive models, followed by neural network-based (e.g., generalized regression neural network), regression tree-based (e.g., random forest), and multivariate linear regression-based techniques. This study identifies promising machine learning techniques among many commonly-used alternatives for monitoring emerging DBPs in SWDNs under data constraints.