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Adaptive Prediction of Water Quality Using Computational Intelligence Techniques

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Computational Science and Its Applications – ICCSA 2017 (ICCSA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10405))

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Abstract

Water is not only vital for ecosystems, wildlife and human consumption, but also for activities such as agriculture, agro-industry, and fishing, among others. These are some of the activities that require water to be developed. However, in the same way as their water use has increased, it has also been detected an accelerated deterioration of its quality. In this sense, to have predictive knowledge about Water Quality (WQ) conditions, can provide a significant relevance to many socio-economic sectors. The premise of this paper is to predict the water quality for different uses of water (aquaculture, irrigation and human consumption) represented by several datasets. This approach is based on Support Vector Regression (SVR) technique configured with the Pearson VII Universal Kernel (PUK), and an evolutionary algorithm of Particle Swarm Optimization (PSO). Experimental results show that the proposed predictive mechanism provides an acceptable prediction accuracy on different water-use datasets. These results indicate that bio-inspired techniques improve the adaptive capacity of a prediction algorithm.

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Acknowledgements

The authors are grateful to the Telematics Engineering Group (GIT) and Environmental Studies Group (GEA) of the University of Cauca, Institute CINARA of the University of Valle, RICCLISA Program and AgroCloud project for supporting this research, and the Administrative Department of Science, Technology and Innovation (Colciencias) for PhD scholarship granted to MSc. Iván Darío López.

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Authors and Affiliations

  1. Grupo de Ingeniería Telemática (GIT), Universidad del Cauca, Popayán, Colombia

    Iván Darío López & Juan Carlos Corrales

  2. Grupo de Estudios Ambientales (GEA), Universidad del Cauca, Popayán, Colombia

    Apolinar Figueroa

Authors
  1. Iván Darío López
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  2. Apolinar Figueroa
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  3. Juan Carlos Corrales
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Correspondence to Iván Darío López .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Trieste, Trieste, Italy

    Giuseppe Borruso

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  9. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  10. University of Trieste, Trieste, Italy

    Alfredo Cuzzocrea

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López, I.D., Figueroa, A., Corrales, J.C. (2017). Adaptive Prediction of Water Quality Using Computational Intelligence Techniques. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10405. Springer, Cham. https://doi.org/10.1007/978-3-319-62395-5_4

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  • DOI: https://doi.org/10.1007/978-3-319-62395-5_4

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  • Print ISBN: 978-3-319-62394-8

  • Online ISBN: 978-3-319-62395-5

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