Some observations on biofouling prediction in pipelines using model trees and artificial neural networks versus logistic regression

Biofouling is the phenomenon of micro-organism attachments to wet surfaces. Complete understanding of the mechanisms and rates of biofilms creations are partially understood therefore forecasting their formations is difficult. This study is on biofouling predictions for water distribution systems pipelines using model trees, artificial neural networks, and logistic regression. The three methods were tested through base runs and sensitivity analysis runs using data from the experiment conducted by Simões et al. (2006). The results showed that none of the models were superior for all cases, therefore a single model could not be recommended. This leads to an important conclusion that utilising 'low cost' modelling methods such as logistic regression can be sufficient for providing reliable estimates for biofilm growth potential. 'Low cost' approaches should be applied prior to invoking expensive models such as data driven methods as the latter might not be needed.