Adaptive Prediction of Water Quality Using Computational Intelligence Techniques

  • Iván Darío LópezEmail author
  • Apolinar Figueroa
  • Juan Carlos Corrales
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10405)


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.


Computational Intelligence Forecasting Particle Swarm Optimization Support Vector Regression Water Quality 



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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Grupo de Ingeniería Telemática (GIT)Universidad del CaucaPopayánColombia
  2. 2.Grupo de Estudios Ambientales (GEA)Universidad del CaucaPopayánColombia

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