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Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process for Machine Learning (GPML) Algorithms for the Prediction of Norovirus Concentration in Drinking Water Supply

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXV

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 10680))

Abstract

Monitoring of Norovirus in drinking water supply is a complicated, rather expensive, process. Norovirus represent a leading cause of acute gastroenteritis in most developed countries. Modeling of general microbial occurrence in drinking water is a very active field of study and provides reliable information for predicting microbial risks in drinking water. In this work, adaptive neuro-fuzzy inference system (ANFIS) and Gaussian Process for Machine Learning (GPML) are proposed as predicting models for the total number of Norovirus in raw surface water in terms of water quality parameters such as water pH, turbidity, conductivity, temperature and rain. The predictive models were based on data from Nødre Romrike Vannverk water treatment plant in Oslo, Norway. Based on the model performance indices used in this study, the GPML model showed comparable accuracy to the ANFIS model. However, the ANFIS model generally demonstrated more superior prediction ability of the number of Norovirus in drinking water, with lower MSE and MAE values relative to the GPML model. In addition, the ability of the ANFIS model to explain potential effects of interactions among the water quality variables on the number of Norovirus in the raw water makes the technique more efficient for use in water quality modeling.

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Acknowledgements

The authors wish to thank the managers of the Nødre Romrike Water Treatment Plant in Oslo for the provision of required data. Thanks to Ricardo Rosado and Mette Myrmel for providing the Norovirus data. This work is part of the project KLIMAFORSK funded by the Research Council of Norway (Project No: 244147/E10). The authors would like to express their sincere thanks to the editor and anonymous reviewers for their suggestions and comments to improve the quality of the paper.

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Authors and Affiliations

  1. Water and Environmental Engineering Group, Department of Marine Operations and Civil Engineering, Faculty of Engineering, Norwegian University of Science and Technology (NTNU), Postboks 1517, 6025, Ålesund, Norway

    Hadi Mohammed & Razak Seidu

  2. Department of ICT and Natural Sciences, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU), Postboks 1517, 6025, Ålesund, Norway

    Ibrahim A. Hameed

Authors
  1. Hadi Mohammed
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  2. Ibrahim A. Hameed
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  3. Razak Seidu
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Correspondence to Hadi Mohammed .

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Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Josef Küng

  3. FAW, University of Linz, Linz, Austria

    Roland Wagner

  4. King Saud bin Abdulaziz University, Riyadh, Saudi Arabia

    Sherif Sakr

  5. King Saud bin Abdulaziz University, Riyadh, Saudi Arabia

    Imran Razzak

  6. King Saud bin Abdulaziz University, Riyadh, Saudi Arabia

    Alshammari Riyad

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Mohammed, H., Hameed, I.A., Seidu, R. (2017). Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process for Machine Learning (GPML) Algorithms for the Prediction of Norovirus Concentration in Drinking Water Supply. In: Hameurlain, A., Küng, J., Wagner, R., Sakr, S., Razzak, I., Riyad, A. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXV. Lecture Notes in Computer Science(), vol 10680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56121-8_4

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  • DOI: https://doi.org/10.1007/978-3-662-56121-8_4

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  • Print ISBN: 978-3-662-56120-1

  • Online ISBN: 978-3-662-56121-8

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