Journal article
Characterizing temporal development of biofilm porosity using artificial neural networks
Water science and technology, Vol.57(12), pp.1867-1872
06/01/2008
Handle:
https://hdl.handle.net/2376/105786
PMID: 18587172
Abstract
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.
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Details
- Title
- Characterizing temporal development of biofilm porosity using artificial neural networks
- Creators
- Raaja Raajan Angathevar Veluchamy - Center for Biofilm Engineering, Montana State University, Bozeman, MT 59717 USAZbigniew Lewandowski - Center for Biofilm Engineering, Montana State University, Bozeman, MT 59717 USA, Department of Civil Engineering, Montana State University, Bozeman, MT 59717 USA E-mail: zl@erc.montana.eduHaluk Beyenal - School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA 99164 USA E-mail: beyenal@wsu.edu
- Publication Details
- Water science and technology, Vol.57(12), pp.1867-1872
- Academic Unit
- Chemical Engineering and Bioengineering, School of
- Identifiers
- 99900547099101842
- Language
- English
- Resource Type
- Journal article