Ensemble of naïve Bayesian approaches for the study of biofilm development in drinking water distribution systems

Various studies have been performed in relation to the influence that a number of characteristics of drinking water distribution systems (DWDSs) have on biofilm development. Nevertheless, their joint influence, apart from a few exceptions, has scarcely been studied due to the complexity of the community and the environment. In this paper, we apply various machine learning algorithms based on naïve Bayesian networks. Alternatives for the base naïve Bayesian model to outperform individual performances while maintaining simplicity are suggested. These alternatives include augmentation of the arcs in the graph, and initial bagging approaches. Finally, a combination of different naïve approaches in a bagging process that produces explanatory hybrid decision trees is proposed. As a result, it is possible to achieve a deeper understanding of the consequences that the interaction of the relevant hydraulic and physical factors of DWDSs has on biofilm development.