We used artificial neural networks (ANN) to compute parameters characterising biofilm structure from biofilm images and to interpolate a limited number of experimental data characterising the effects of nutrient concentration and flow velocity on the areal porosity of biofilms. ANN were trained using a set of experimental data characterising structural parameters of biofilms of Pseudomonas aeruginosa (ATCC #700829), Pseudomonas fluorescens (ATCC #700830) and Klebsiella pneumoniae (ATCC #700831) for various flow velocities and glucose concentrations. We used 80% of the data to train ANN and 10% of the data to validate the results, which is routinely carried out as a countermeasure against overtraining. Trained ANN were used to interpolate into the data set and evaluate the missing 10% of the data. To compare ANN accuracy in evaluating the missing data with the accuracies achieved using other interpolation algorithms, we used spline, cubic, linear and nearest-neighbour interpolation algorithms to evaluate the missing data. ANN estimates were consistently closer to the experimental data than the estimates made using the other methods.
Characterizing temporal development of biofilm porosity using artificial neural networks
Raaja Raajan Angathevar Veluchamy, Zbigniew Lewandowski, Haluk Beyenal; Characterizing temporal development of biofilm porosity using artificial neural networks. Water Sci Technol 1 June 2008; 57 (12): 1867–1872. doi: https://doi.org/10.2166/wst.2008.608
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