Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers)

  • E. S. Salami
  • M. EhteshamiEmail author
Original Paper


In this analysis, three input parameters temperature, pH and electrical conductivity were chosen due to their easy and less costly measurement technique, and a package of six models were presented for estimating the concentrations of dissolved oxygen, DO percentage, biological oxygen demand, chloride, alkalinity and total hardness. 3001 data sets (a 3001 × 8 data array) were used to training the models. The models have been tested in order to verify their prediction values, and the resulted R factor (the rate of precision) for each model equals to 0.93, 0.95, 0.77, 0.82, 0.85 and 0.92, respectively. This proves that the package can be used to estimate the concentrations of water quality parameters with accuracy close to the reality. The River data collected from 210 monitoring stations located in all over Ireland have been used. The data set covers different conditions and makes the model applicable in many different places and conditions. For development of all models, feed-forward algorithm used for training, as well as the Levenberg–Marquardt and tansign(x) functions as learning and transfer functions.


Artificial neural networks Ireland Rivers Modeling Water characteristics Water quality 



The authors are grateful to Dr Sohrab Soori for their editorial and revision assistance. Also, they are thankful of “Water Quality Environmental Protection Agency, Ireland,” for providing data sets.


  1. Abraham A (2005) Artificial neural networks. Oklahoma State University, Stillwater, pp 901–908Google Scholar
  2. Akilandeswari S, Adline MH (2013) Prediction of BOD values in engineering work industrial effluent by Anfis modeling. Int J Res Pure Appl Phys 3(2):7–9Google Scholar
  3. Anctila F, Filion M, Tournebizeb J (2009) A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment. Ecol Model 220:879–887CrossRefGoogle Scholar
  4. Chitsazan M, Rahmani R, Neyamadpour A (2013) Groundwater level simulation using artificial neural network: a case study from Aghili plain, urban area of Gotvand, south-west Iran. JGeope 3(1):35–46Google Scholar
  5. Chu HB, Lu WX, Zhang L (2013) Application of artificial neural network in environmental water quality assessment. J Agric Sci Technol 15:343–356Google Scholar
  6. Diamantopoulou MJ, Antonopoulos VZ, Papamichail DM (2005) The use of a neural network technique for the prediction of water quality parameters of Axios River in Northern Greece. Eur Water 11(12):55–62Google Scholar
  7. Donohue I, Irvine K (2008) Quantifying variability within water samples: the need for adequate subsampling. Water Res 42:476–482CrossRefGoogle Scholar
  8. Ghaffari A, Abdollahi H, Khoshayand MR, Bozchalooi IS, Dadgar A, Rafiee-Tehrani M (2006) Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int J Pharm 327:126–138CrossRefGoogle Scholar
  9. Gustavo Andres Cuesta Cordoba Ing (2011) Using of artificial neural network for evaluation and prediction of some drinking water quality parameters within a water distribution system. Water management and water structures, Juniorstav, pp 1–11Google Scholar
  10. Haughey I (2010) The return on investment (ROI) of data modeling. CA, Erwin, March, pp 1–18Google Scholar
  11. Jalili Ghazi Zade M, Noori R (2008) Prediction of municipal solid waste generation by use of artificial neural network: a case study of Mashhad. Environ Res 2(1):13–22Google Scholar
  12. Kim M, Gilley JE (2008) Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas. Comput Electron Agric 64:268–275CrossRefGoogle Scholar
  13. Koncsos T (2010) The application of neural networks for solving complex optimization problems in modeling. In: Conference of Junior Researchers in Civil Engineering pp 97–102Google Scholar
  14. Kuo YM, Liu CW, Lin KH (2004) Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of black foot disease in Taiwan. Water Res 38:148–158CrossRefGoogle Scholar
  15. Lihua C, Shengquan M, Li LI (2008) A model to evaluate do of river based on artificial neural network and style book. J Hainan Normal Univ Nat Sci 21(4):372–376Google Scholar
  16. McKnighta S, Fundera SG, Rasmussenb JJ, Finkelc M, Binninga PJ, Bjerga PL (2010) An integrated model for assessing the risk of TCE groundwater contamination to human receptors and surface water ecosystems. Ecol Eng 36:1126–1137CrossRefGoogle Scholar
  17. Menhaj MB (2008) Fundamental of neural network, vol 1. Industrial Amir Kabir University, TehranGoogle Scholar
  18. Nadiri A (2007) Predicting groundwater level surrounding Tabriz city. Msd. Thesis, Tabriz UniversityGoogle Scholar
  19. Nejadkoorki F, Baroutian S (2011) Forecasting extreme PM10 concentrations using artificial neural networks. J Environ Res 6(1):277–284Google Scholar
  20. Panda Rabindra K, Pramanik N, Bala B (2010) Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Comput Geosci 36:735–745CrossRefGoogle Scholar
  21. Patki VK, Shirihari S, Manu B (2013) Water quality prediction in distribution system using Cascade feed forward neural network. Int J Adv Technol Civil Eng, ISSN: 2231–5721, 2(1):84–91Google Scholar
  22. Pradhan B, Pirasteh S (2011) Hydro-chemical analysis of the ground water of the basaltic catchments: upper bhatsai region, Maharashtra. Open Hydrol J 5:51–57CrossRefGoogle Scholar
  23. Rak A (2013) Water turbidity modelling during water treatment processes using artificial neural networks. Int J Water Sci 2(3):1–10CrossRefGoogle Scholar
  24. Rich D, Washo BD, Paladini A (2006) Rapid field test for nitrate and ammonia in reclaimed water. Everglades Res Educ Center 2:2006Google Scholar
  25. Rounds SA (2002) Development of a neural network model for dissolved oxygen in the Tualatin River. In: Oregon Second Federal Interagency hydrologic modeling conference, Las Vegas, Nevada, July 29–August 1, pp 1–13Google Scholar
  26. Schleiter IM, Borchardt D, Wagner R, Dapper T, Schmidt KD, Schmidt HH, Werner H (1999) Modeling water quality, bioindication and population dynamics in lotic ecosystems using neural networks. Ecol Model 120:271–286CrossRefGoogle Scholar
  27. Scholten H, Kassahun A, Refsgaard JC, Kargas T, Gavardinas C, Beulens AJM (2007) A methodology to support multidisciplinary model-based water management. Environ Model Softw 22:743–759CrossRefGoogle Scholar
  28. Setiono R (2001) Feed-forward neural network construction using cross validation. Neural Comput 13(12):2865–2877CrossRefGoogle Scholar
  29. Sevostianov I, Shrestha M (2010) Cross-property connections between overall electric conductivity and fluid permeability of a random porous media with conducting skeleton. Int J Eng Sci 48:1702–1708CrossRefGoogle Scholar
  30. Stockholm International Water Institute and Elsevier (2012) The water and food nexus: trends and development of the research landscapeGoogle Scholar
  31. Svozil D, KvasniEka V, Pospichal J (1997) Introduction to multi-layer feed-forward neural networks. Chemometr Intell Lab Syst 39:43–62CrossRefGoogle Scholar
  32. United States Environment Protection Agency (2013) Total Alkalinity. Retrieved 6 Mar 2013Google Scholar
  33. Varnell LM, Evans DA, Bilkovic DM, Olney JE (2008) Estuarine surface water allocation: a case study on the interactive role of science in support of management. Environ Sci Policy 11:602–612CrossRefGoogle Scholar
  34. Wurts WA (2002) Alkalinity and hardness in production ponds. World Aquac 33:16–17Google Scholar
  35. Zhang Z, Wang X, Ou Y (2010) Water simulation method based on BPNN response and analytic geometry. Proc Environ Sci 2:446–453CrossRefGoogle Scholar

Copyright information

© Islamic Azad University (IAU) 2015

Authors and Affiliations

  1. 1.KN Toosi University of TechnologyTehranIran
  2. 2.Environmental Engineering DepartmentKN Toosi University of TechnologyTehranIran

Personalised recommendations