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

  • Iván Darío LópezEmail author
  • Cristian Heidelberg Valencia
  • Juan Carlos Corrales
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10710)

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.

Keywords

Classification Machine learning Multiple classifier systems Water quality 

Notes

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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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Grupo de Ingeniería Telemática (GIT)Universidad del CaucaPopayánColombia

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