Real-Time Multi-Class Classification of Water Quality Using MLP and Ensemble Learning

Essa Q. Shahra*, Shadi Basurra, Wenyan Wu

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    1 Citation (SciVal)

    Abstract

    The major goal of water management planning and the iterative evaluation of operational policies and procedures is to ensure that good water quality is always maintained. Effective water monitoring requires examining many water samples, which is a time-consuming and labour-intensive process that takes a lot of effort. This paper aims to evaluate the quality of drinking water samples with high accuracy by using multi-class classification models: multilayer perceptron (MLP) and ensemble learning. Real datasets with different sizes that include the essential water quality parameters have been used to train and test the developed models. The results showed the effectiveness of the developed models in detecting water contamination with high accuracy in both datasets used. The results demonstrate that bagging Ensemble learning outperforms the multilayer perceptron with an overall accuracy of 94% for station-A and 92% for station-B compared to MLP, which shows an overall accuracy of 89% for station-A and 87% for station-B.
    Original languageEnglish
    Title of host publicationProceedings of 8th International Congress on Information and Communication Technology - ICICT 2023
    EditorsXin-She Yang, R. Simon Sherratt, Nilanjan Dey, Amit Joshi
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages481-491
    Number of pages11
    ISBN (Print)9789819930425
    DOIs
    Publication statusPublished (VoR) - 26 Oct 2023
    Event8th International Congress on Information and Communication Technology, ICICT 2023 - London, United Kingdom
    Duration: 20 Feb 202323 Feb 2023

    Publication series

    NameLecture Notes in Networks and Systems
    Volume695 LNNS
    ISSN (Print)2367-3370
    ISSN (Electronic)2367-3389

    Conference

    Conference8th International Congress on Information and Communication Technology, ICICT 2023
    Country/TerritoryUnited Kingdom
    CityLondon
    Period20/02/2323/02/23

    Funding

    This research is supported by European Union’s Horizon 2020 research and innovation program Under the Marie Skłodowska-Curie-Innovative Training Networks (ITN)-IoT4Win-Internet of Things for Smart Water Innovative Network (765921).

    FundersFunder number
    Horizon 2020 Framework Programme765921

      Keywords

      • Classification
      • Ensemble learning
      • MLP
      • Water Quality

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