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Deep Learning for Water Quality Classification in Water Distribution Networks

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Proceedings of the 22nd Engineering Applications of Neural Networks Conference (EANN 2021)

Part of the book series: Proceedings of the International Neural Networks Society ((INNS,volume 3))

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Abstract

Maintaining high water quality is the main goal for water management planning and iterative evaluation of operating policies. For effective water monitoring, it is crucial to test a vast number of drinking water samples that is time-consuming and labour-intensive. The primary objective of this study is to determine, with high accuracy, the quality of drinking water samples by machine learning classification models while keeping computation time low. This paper aims to investigate and evaluate the performance of two supervised classification algorithms, including artificial neural network (ANN) and support vector machine (SVM) for multiclass water classification. The evaluation uses the confusion matrix that includes all metrics ratios, such as true positive, true negative, false positive, and false negative. Moreover, the overall accuracy and f1-score of the models are evaluated. The results demonstrate that ANN outperformed the SVM with an overall accuracy of 94% in comparison to SVM, which shows an overall accuracy of 89%.

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Acknowledgment

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)

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Authors and Affiliations

  1. Faculty of Computing, Engineering and Built Environment, Birmingham City University, Birmingham, B4 7XG, UK

    Essa Q. Shahra, Wenyan Wu & Shadi Basurra

  2. Singular Logic S.A., Athens, Greece

    Stamatia Rizou

Authors
  1. Essa Q. Shahra
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  2. Wenyan Wu
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  3. Shadi Basurra
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  4. Stamatia Rizou
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Corresponding authors

Correspondence to Essa Q. Shahra or Wenyan Wu .

Editor information

Editors and Affiliations

  1. School of Engineering, Department of Civil Engineering, Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Faculty of Applied Sciences, University of Sunderland, Sunderland, UK

    John Macintyre

  3. School of Computing and Digital Technologies, Teesside University, Middlesbrough, UK

    Chrisina Jayne

  4. University of the West of England, Bristol, UK

    Elias Pimenidis

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Shahra, E.Q., Wu, W., Basurra, S., Rizou, S. (2021). Deep Learning for Water Quality Classification in Water Distribution Networks. In: Iliadis, L., Macintyre, J., Jayne, C., Pimenidis, E. (eds) Proceedings of the 22nd Engineering Applications of Neural Networks Conference. EANN 2021. Proceedings of the International Neural Networks Society, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-80568-5_13

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