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Building an Early Warning Model for Detecting Environmental Pollution of Wastewater in Industrial Zones

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Advances on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 96))

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Abstract

In this paper, we present two soft computing techniques, which are support vector regression (SVR) and fuzzy logic, to build an early warning model for detecting environmental pollution of waste-water in industrial zones. To determine the proper number of inputs for the model, we use an algorithm to find the embedding dimension space for a time series. Our proposed model, which has a high accuracy and short training time, to helps waste-water processing station operators take early action and avoid environmental pollution.

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Acknowledgments

We would like to acknowledge the Hanoi University of Industry for supporting our work.

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Authors and Affiliations

  1. Department of Information Technology, Hanoi University of Industry, Number 298, Cau Dien Street, Bac Tu Liem District, Hanoi, Vietnam

    Nghien Nguyen Ba

  2. Chihuahua, Mexico

    Ricardo Rodriguez Jorge

Authors
  1. Nghien Nguyen Ba
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  2. Ricardo Rodriguez Jorge
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Corresponding author

Correspondence to Nghien Nguyen Ba .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Antwerp, Antwerpen, Belgium

    Peter Hellinckx

  3. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

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Ba, N.N., Jorge, R.R. (2020). Building an Early Warning Model for Detecting Environmental Pollution of Wastewater in Industrial Zones. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_78

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  • DOI: https://doi.org/10.1007/978-3-030-33509-0_78

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33508-3

  • Online ISBN: 978-3-030-33509-0

  • eBook Packages: EngineeringEngineering (R0)

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