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Abstract
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.
Acknowledgements
This work has been performed with the support of the project IDAWAS, DPI2009-11591 of the Dirección General de Investigación del Ministerio de Ciencia e Innovación (Spain) and ACOMP/2011/188 of the Conselleria de Educació of the Generalitat Valenciana. We want to express our gratitude to the research grant (FPI), Ministerio de Ciencia e Innovación (ref.: BES-2010-039145). The use of English in this paper was revised by John Rawlins.