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

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    6 Citations (Scopus)

    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 perception 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. Copyright © 2007 IAHS Press.

    Original languageEnglish
    Title of host publicationIAHS-AISH Publication - Water Quality and Sediment Behaviour of the Future: Predictions for the 21st Century
    Pages181-187
    Number of pages7
    Edition314
    Publication statusPublished - 2007
    EventInternational Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG) - Perugia, Italy
    Duration: 2 Jul 200713 Jul 2007

    Conference

    ConferenceInternational Symposium: Quantification and Reduction of Predictive Uncertainty for Sustainable Water Resources Management - 24th General Assembly of the International Union of Geodesy and Geophysics (IUGG)
    CountryItaly
    CityPerugia
    Period2/07/0713/07/07

    Keywords

    • Kohonen self-organising map
    • Neural networks
    • Primary clarifier modelling
    • Wastewater treatment plant

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  • Cite this

    Rustum, R., Adeloye, A., & Simala, A. (2007). Kohonen self-organising map (KSOM) extracted features for enhancing MLP-ANN prediction models of BOD5. In IAHS-AISH Publication - Water Quality and Sediment Behaviour of the Future: Predictions for the 21st Century (314 ed., pp. 181-187)