Replacing outliers and missing values from activated sludge data using kohonen self-organizing map

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    53 Citations (Scopus)

    Abstract

    Modeling the activated sludge wastewater treatment plant plays an important role in improving its performance. However, there are many limitations of the available data for model identification, calibration, and verification, such as the presence of missing values and outliers. Because available data are generally short, these gaps and outliers in data cannot be discarded but must be replaced by more reasonable estimates. The aim of this study is to use the Kohonen self-organizing map (KSOM), unsupervised neural networks, to predict the missing values and replace outliers in time series data for an activated sludge wastewater treatment plant in Edinburgh, U.K. The method is simple, computationally efficient and highly accurate. The results demonstrated that the KSOM is an excellent tool for replacing outliers and missing values from a high-dimensional data set. A comparison of the KSOM with multiple regression analysis and back-propagation artificial neural networks showed that the KSOM is superior in performance to either of the two latter approaches. © 2007 ASCE.

    Original languageEnglish
    Pages (from-to)909-916
    Number of pages8
    JournalJournal of Environmental Engineering
    Volume133
    Issue number9
    DOIs
    Publication statusPublished - 2007

    Keywords

    • Activated sludge
    • Mathematical models
    • Neural networks
    • Wastewater management
    • Water treatment plants

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