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A Data Mining Tool for Water Uses Classification Based on Multiple Classifier Systems

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Book cover Machine Learning, Optimization, and Big Data (MOD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10710))

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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. 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 conditions, can provide a significant relevance to many socio-economic sectors. In this paper, we present an approach to predict the water quality for different uses (aquaculture, irrigation, and human consumption) discovering knowledge from several datasets of American and Andean Watersheds. This proposal is based on Multiple Classifier Systems (MCS), including Bagging, Stacking, and Random Forest. Models as Naïve Bayes, KNN, C4.5, and Multilayer Perceptron are combined to increase the accuracy of the classification task. The experimental results obtained show that Random Forest and Stacking expose acceptable precision on different water-use datasets. However, Bagging with C4.5 was the most appropriate architecture for the problem addressed. These results indicate that MCS techniques can be used for improving accuracy and generalization capacity of the prediction tools used by stakeholder involvement in the water quality process.

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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 Colciencias (Colombia) 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, Cristian Heidelberg Valencia & Juan Carlos Corrales

Authors
  1. Iván Darío López
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  2. Cristian Heidelberg Valencia
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  3. Juan Carlos Corrales
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Corresponding author

Correspondence to Iván Darío López .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Italy

    Giuseppe Nicosia

  2. University of Florida, Gainesville, FL, USA

    Panos Pardalos

  3. University of Catania, Catania, Italy

    Giovanni Giuffrida

  4. Harvard University, Cambridge, MA, USA

    Renato Umeton

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López, I.D., Valencia, C.H., Corrales, J.C. (2018). A Data Mining Tool for Water Uses Classification Based on Multiple Classifier Systems. In: Nicosia, G., Pardalos, P., Giuffrida, G., Umeton, R. (eds) Machine Learning, Optimization, and Big Data. MOD 2017. Lecture Notes in Computer Science(), vol 10710. Springer, Cham. https://doi.org/10.1007/978-3-319-72926-8_30

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  • DOI: https://doi.org/10.1007/978-3-319-72926-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72925-1

  • Online ISBN: 978-3-319-72926-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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