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
Article

Abstract

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.

Keywords

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

References

  1. Abdul-Wahab SA, Al-Alawi SM (2002) Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks. Environ Modell Softw 17:219–228CrossRefGoogle Scholar
  2. Abghari H, Ahmadi H, Besharat S, Rezaverdinejad V (2012) Prediction of daily pan evaporation using wavelet neural networks. Water Resour Manag 26(12):3639–3652CrossRefGoogle Scholar
  3. Al-Alawi SM, Abdul-Wahab SA, Bakheit CS (2008) Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environ Modell Softw 23(4):396–403CrossRefGoogle Scholar
  4. Alonso Fernández JR, Díaz Muñiz C, García Nieto PJ, de Cos Juez FJ, Sánchez Lasheras F, Roqueñí MN (2013) Forecasting the cyanotoxins presence in fresh waters: A new model based on genetic algorithms combined with the MARS technique. Ecol Eng 53:68–78CrossRefGoogle Scholar
  5. Andrews MJ (1984) Thames estuary: pollution and recovery. In: Sheehan PJ, Miller DR, Butler GC, Bourdeau PH (eds) Effects of pollutants at the ecosystem level, Scope 22. John Wiley& Sons, New York, pp 195–227Google Scholar
  6. Aslan-Yilmaz A, Okus E, Övez S (2004) Bacteriological indicators of anthropogenic impact prior to and during the recovery of water quality in an extremely polluted estuary, Golden Horn, Turkey. Mar Pollut Bull 49:951–958CrossRefGoogle Scholar
  7. Babel MS, Shinde VR (2011) Identifying prominent explanatory variables for water demand prediction using artificial neural networks: a case study of Bangkok. Water Resour Manag 25(6):1653–1656CrossRefGoogle Scholar
  8. Barnes DJ, Chu D (2010) Introduction to modeling for biosciences. Springer, New YorkCrossRefGoogle Scholar
  9. Bartram J, Rees G (2000) Monitoring bathing waters: a practical guide to the design and implementation of assessments and monitoring programmes. E & FN SPON, LondonCrossRefGoogle Scholar
  10. Behrang MA, Assareh E, Ghanbarzadeh A, Noghrehabadi AR (2010) The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data. Sol Energy 84(8):1468–1480CrossRefGoogle Scholar
  11. Bishop CM (2008) Neural networks for pattern recognition. Oxford University Press, New YorkGoogle Scholar
  12. Clark RB (2001) Marine pollution. Oxford University Press, New YorkGoogle Scholar
  13. Díaz Muñiz C, García Nieto PJ, Alonso Fernández JR, Martínez Torres J, Taboada J (2012) Detection of outliers in water quality monitoring samples using functional data analysis in San Esteban estuary (Northern Spain). Sci Total Environ 439:54–61CrossRefGoogle Scholar
  14. Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy, L-327 LuxembourgGoogle Scholar
  15. Elkamel A, Abdul-Wahab S, Bouhamra W, Alper E (2001) Measurement and prediction of ozone levels around a heavily industrialized area: a neural network approach. Adv Environ Res 5(1):47–59CrossRefGoogle Scholar
  16. Fausset LV (1993) Fundamentals of neural networks: architectures, algorithms and applications. Pearson, New YorkGoogle Scholar
  17. France RL, Peters RH (1995) Predictive model of the effects on lake metabolism of decreased airborne litterfall through riparian deforestation. Conserv Biol 9(6):1578–1586Google Scholar
  18. Freedman D, Pisani R, Purves R (2007) Statistics. W. W. Norton & Company, New YorkGoogle Scholar
  19. García Nieto PJ, Martínez Torres J, Araújo Fernández M, Ordóñez Galán C (2012a) Support vector machines and neural networks used to evaluate paper manufactured using Eucalyptus globulus. Appl Math Model 36:6137–6145CrossRefGoogle Scholar
  20. García Nieto PJ, Alonso Fernández JR, Sánchez Lasheras F, de Cos Juez FJ, Díaz Muñiz C (2012b) A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique. Sci Total Environ 430:88–92CrossRefGoogle Scholar
  21. García Nieto PJ, Alonso Fernández JR, de Cos Juez FJ, Sánchez Lasheras F, Díaz Muñiz C (2013) Hybrid modelling based on support vector regression with genetic algorithms in forecasting the cyanotoxins presence in the Trasona reservoir (Northern Spain). Environ Res 122:1–10CrossRefGoogle Scholar
  22. García-Barcina JM, Oteiza M, de la Sota A (2002) Modelling the faecal coliform concentrations in the Bilbao estuary. Hydrobiologia 475(476):213–219CrossRefGoogle Scholar
  23. Haykin SO (2008) Neural networks and learning machines. Prentice Hall, New YorkGoogle Scholar
  24. Hea B, Oki T, Sun F, Komori D, Kanae S, Wang Y, Kim H, Yamazaki D (2011) Estimating monthly total nitrogen concentration in streams by using artificial neural network. J Environ Manage 92(1):172–177CrossRefGoogle Scholar
  25. Heaton J (2012) Introduction to the math of neural networks. Heaton Research, New YorkGoogle Scholar
  26. Ingram JC, Dawson TP, Whittaker RJ (2005) Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sens Environ 94(4):491–507CrossRefGoogle Scholar
  27. Li Y, Migliaccio K (2010) Water quality concepts, sampling, and analyses. CRC Press, Boca Raton (FL)CrossRefGoogle Scholar
  28. MacCulloch WS, Pitts WS (1943) A logical calculus of the ideas immanent in nervous activity. B Math Biophys 5:115–133CrossRefGoogle Scholar
  29. Mustafa MR, Rezaur RB, Saiedi S, Isa MH (2012) River suspended sediment prediction using various multilayer perceptron neural network training algorithms—a case study in Malaysia. Water Resour Manag 26(7):1879–1897CrossRefGoogle Scholar
  30. Nagesh Kumar D, Srinivasa Raju K, Sathist T (2004) River flow forecasting using recurrent neural networks. Water Resour Manag 18(2):143–161CrossRefGoogle Scholar
  31. Palani S, Liong SY, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56:1586–1597CrossRefGoogle Scholar
  32. Pausas JG (2004) Changes in fire and climate in the eastern Iberian Peninsula (Mediterranean basin). Climatic Change 63(3):337–350CrossRefGoogle Scholar
  33. Rabalais NN, Turner RE, Justic D, Díaz RJ (2009) Global change and eutrophication of coastal waters. ICES J Mar Sci 66:1528–1537CrossRefGoogle Scholar
  34. Rahimikhoob A (2010) Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment. Renew Energ 35(9):2131–2135CrossRefGoogle Scholar
  35. Sengorur B, Dogan E, Koklu R, Samandar A (2006) Dissolved oxygen estimation using artificial neural network for water quality control. Fresen Environ Bull 15(9a):1064–1067Google Scholar
  36. Verity PG, Yoder JA, Bishop SS, Nelson JR, Craven DB, Blanton JO, Robertson CY, Tronzo CR (1993) Composition, productivity and nutrient chemistry of a coastal ocean planktonic food web. Cont Shelf Res 13:741–776CrossRefGoogle Scholar
  37. Vigil KJ (2003) Clean water: an introduction to water quality and water pollution control. Oregon State University Press, PortlandGoogle Scholar

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

Personalised recommendations