Water Resources Management

, Volume 28, Issue 2, pp 319–331 | Cite as

Turbidity Prediction in a River Basin by Using Artificial Neural Networks: A Case Study in Northern Spain

  • C. Iglesias
  • J. Martínez Torres
  • P. J. García Nieto
  • J. R. Alonso Fernández
  • C. Díaz Muñiz
  • J. I. Piñeiro
  • J. Taboada


Chemical and physical-chemical parameters define water quality and are involved in water body type and habitat determination. They support a biological community of a certain ecological status. Water quality controls involve a large number of measurements of variables and observations according to the European Water Framework Directive (Directive 2000/60/EC). In some cases, such as areas with especially critical uses or points in which potential pollution episodes are expected, the automatic monitoring is recommended. However, the chemical and physical-chemical measurements are costly and time consuming. Turbidity is shown as a key variable for the water quality control and it is also an integrative parameter. For this reason, the aim of this work is focused on this main parameter through the study of the influence of several water quality parameters on it. The artificial neural networks (ANNs) have been used in a wide range of biological problems with promising results. Bearing this in mind, turbidity values have been predicted here by using artificial neural networks (ANNs) from the remaining measured water quality parameters with success taking into account the synergistic interactions between the input variables in the Nalón river basin (Northern Spain). Finally, the main conclusions of this study are exposed.


Artificial neural networks (ANNs) Water quality monitoring Water Framework Directive (WFD) Water pollution 



This study was possible thanks to the experimental dataset from the Automated Water Quality Information System (AWQIS) located in the basin of the Nalón river collected by the Cantabrian Basin Authority (Ministry of Agriculture, Food and Environment of the Government of Spain). At the same time, the authors wish to acknowledge the computational support provided by the Department of Mathematics at University of Oviedo and ‘Centro Universitario de la Defensa’ at University of Zaragoza. Additionally, Carla Iglesias acknowledges her grant FPU 12/02283 from the Spanish Ministry of Education. English grammar and spelling of the manuscript have been revised by a native person.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • C. Iglesias
    • 1
  • J. Martínez Torres
    • 2
  • P. J. García Nieto
    • 3
  • J. R. Alonso Fernández
    • 4
  • C. Díaz Muñiz
    • 4
  • J. I. Piñeiro
    • 1
  • J. Taboada
    • 1
  1. 1.Department of Natural Resources and Environmental EngineeringUniversity of VigoVigoSpain
  2. 2.Centro Universitario de la Defensa, Academia MilitarZaragozaSpain
  3. 3.Department of Mathematics, Faculty of SciencesUniversity of OviedoOviedoSpain
  4. 4.Cantabrian Basin Authority, Spanish Ministry of Agriculture, Food and EnvironmentOviedoSpain

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