Water Quality and Sediment Behaviour of the Future: Predictions for the 21st Century (Proceedings of Symposium HS2005 at IUGG2007, Perugia, July 2007). IAHS Publ. 314, 2007, 181-187

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Kohonen self-organising map (KSOM) extracted features for enhancing MLP-ANN prediction models of BOD₅

Rabee Rustum¹, Adebayo Adeloye¹ & Aurore Simala²

1 School of the Built Environment, Heriot-Watt University, Edinburgh EH14 4AS, UK

a.j.adeloye@hw.ac.uk

2 15 bis, allée Marie, F-93360 Neuilly-Plaisance, France

Abstract This paper presents the results of developing a model to predict the concentrations of biological oxygen demand (BOD₅), in the effluent of the primary clarifier of an activated sludge wastewater treatment plant, using other easily measurable water quality parameters. The model is based on the Kohonen self-organising map (KSOM) and multi-layered perceptron artificial neural networks (MLP-ANN). The KSOM was used to extract the features of the measured data and to deal with the effects of noise and missing values. The best map units of the measurement vectors over the KSOM were used as inputs to the MLP-ANN to reduce the effects of noise and uncertainty in the measurement data, and to replace the missing elements in these measurements. The results of the KSOM-ANN modelling strategy were found to be better than those obtained by the MLP-ANN trained using the raw measurement data.

Key words wastewater treatment plant; primary clarifier modelling; neural networks; Kohonen self-organising map

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