Health Scope

Published by: Kowsar

Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran

Alireza Shakeri Abdolmaleki 1 , Ahmad Gholamalizadeh Ahangar 2 , * and Jaber Soltani 3
Authors Information
1 Department of Water Engineering, Faculty of Soil and Water, University of Zabol, Zabol, IR Iran
2 Department of Soil Sciences, Faculty of Soil and Water, University of Zabol, Zabol, IR Iran
3 Department of Water Engineering, Abureyhan Campus, University of Tehran,Tehran, Ir Iran
Article information
  • Health Scope: May 15, 2013, 2 (1); 31-38
  • Published Online: May 11, 2013
  • Article Type: Research Article
  • Received: December 17, 2012
  • Revised: February 26, 2013
  • Accepted: March 6, 2013
  • DOI: 10.17795/jhealthscope-9828

To Cite: Shakeri Abdolmaleki A, Gholamalizadeh Ahangar A, Soltani J. Artificial Neural Network (ANN) Approach for Predicting Cu Concentration in Drinking Water of Chahnimeh1 Reservoir in Sistan-Balochistan, Iran, Health Scope. 2013 ; 2(1):31-38. doi: 10.17795/jhealthscope-9828.

Abstract
Copyright © 2013, Health Promotion Research Center. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Materials and Methods
4. Results
5. Discussion
6. Conclusions
Acknowledgements
Footnotes
References
  • 1. Boyacioglu H. Development of a water quality index based on a European classification scheme. Water Sa. 2009; 33(1)
  • 2. Khalil B, Ouarda T, St-Hilaire A. Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. J Hydro. 2011; 405(3): 277-87
  • 3. Liou SM, Lo SL, Wang SH. A generalized water quality index for Taiwan. Environ Monit Assess. 2004; 96(1-3): 35-52[PubMed]
  • 4. Fernández N, Ramírez A, Solano F. Revista Bistua. Physicochemical water quality Indices-A comparative review. 2004; 1(1): 19-30
  • 5. Flores JC. Comments to the use of water quality indices to verify the impact of Cordoba City (Argentina) on Suquia river. Water Res. 2002; 36(18): 4664-6[PubMed]
  • 6. Zhang W, Feng H, Chang J, Qu J, Xie H, Yu L. Heavy metal contamination in surface sediments of Yangtze River intertidal zone: an assessment from different indexes. Environ Pollut. 2009; 157(5): 1533-43[DOI][PubMed]
  • 7. Singh KP, Malik A, Mohan D, Sinha S. Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study. Water Res. 2004; 38(18): 3980-92[DOI][PubMed]
  • 8. Finkelman RB. Health benefits of geologic materials and geologic processes. Int J Environ Res Public Health. 2006; 3(4): 338-42[PubMed]
  • 9. Llanos RM, Mercer JF. The molecular basis of copper homeostasis copper-related disorders. DNA Cell Biol. 2002; 21(4): 259-70[DOI][PubMed]
  • 10. Chen J, Chang N, Shieh W. Assessing wastewater reclamation potential by neural network model. Engin Appl Sartif Intel. 2003; 16(2): 149-57
  • 11. Kurunç A, Yürekli K, Çevik O. Performance of two stochastic approaches for forecasting water quality and streamflow data from Yeşilιrmak River, Turkey. Env Model Software. 2005; 20(9): 1195-200
  • 12. Wu HJ, Lin ZY, Gao SL. The application of artificial neural networks in the resources and environment. Resources and Environment in the Yangtze Basin. 2000; 9(2): 241-6
  • 13. Xiang S, Liu Z, Ma L. Study of multivariate linear regression analysis model for ground water quality prediction. Guizhou Sci. 2006; 24(1): 60-2
  • 14. Niu Z, Zhang H, Liu H. Application of neural network to prediction of coastal water quality. J Tianjin Polytechnic Uni. 2006; 25(2): 89-92
  • 15. Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S. Application of neural networks to modelling nonlinear relationships in ecology. Ecological Model. 1996; 90(1): 39-52
  • 16. Hanbay D, Turkoglu I, Demir Y. Prediction of wastewater treatment plant performance based on wavelet packet decomposition and neural networks. Expert Systems Appl. 2008; 34(2): 1038-43
  • 17. Messikh N, Samar M, Messikh L. Neural network analysis of liquid-liquid extraction of phenol from wastewater using TBP solvent. Desalination. 2007; 208(1): 42-8
  • 18. Smits J, Breedveld L, Derksen M, Kateman G, Balfoort H, Snoek J. Pattern classification with artificial neural networks: classification of algae, based upon flow cytometer data. Analytica chimica acta. 1992; 258(1): 11-25
  • 19. Bowers J, Shedrow C. Predicting stream water quality using artificial neural networks. Miscellaneous series Westinghouse Savannah River Co. 2000; (112)
  • 20. Kuo JT, Hsieh MH, Lung WS, She N. Using artificial neural network for reservoir eutrophication prediction. Ecological Model. 2007; 200(1): 171-7
  • 21. Kuo YM, Liu CW, Lin KH. Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan. Water Res. 2004; 38(1): 148-58[DOI][PubMed]
  • 22. Ru-zhong L. Advance and trend analysis of theoretical methodology for water quality forecast. J Hefei UniTech (Natural Sci). 2006; 1: 7
  • 23. Gallant SI. Neural network learning and expert systems: MIT press. 1993;
  • 24. Smith M. Neural networks for statistical modeling: Thomson Learning. 1993;
  • 25. Dreyfus G, Martinez JM, Samuelides M, Gordon MB, Badran F, Thiria S. Apprentissage statistique: Réseaux de neurones-Cartes topologiques-Machines à vecteurs supports: Eyrolles. 2011;
  • 26. Karunanithi N, Grenney WJ, Whitley D, Bovee K. Neural networks for river flow prediction. J Com Civil Eng. 1994; 8(2): 201-20
  • 27. Hussain M, Shafiur Rahman M, Ng C. Prediction of pores formation (porosity) in foods during drying: generic models by the use of hybrid neural network. J Food Eng. 2002; 51(3): 239-48
  • 28. Jorjani E, Chehreh Chelgani S, Mesroghli S. Application of artificial neural networks to predict chemical desulfurization of Tabas coal. Fuel. 2008; 87(12): 2727-34
  • 29. Razavi MA, Mortazavi A, Mousavi M. Dynamic modelling of milk ultrafiltration by artificial neural network. J Membrane Sci. 2003; 220(1): 47-58
  • 30. Ghaffari A, Abdollahi H, Khoshayand MR, Bozchalooi IS, Dadgar A, Rafiee-Tehrani M. Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int J Pharm. 2006; 327(1-2): 126-38[DOI][PubMed]
  • 31. Torrecilla J, Otero L, Sanz P. Optimization of an artificial neural network for thermal/pressure food processing: Evaluation of training algorithms. Com Elec Agri. 2007; 56(2): 101-10
  • 32. Erzin Y, Rao BH, Singh D. Artificial neural network models for predicting soil thermal resistivity. Int J Thermal Sci. 2008; 47(10): 1347-58
  • 33. Karul C, Soyupak S, Çilesiz AF, Akbay N, Germen E. Case studies on the use of neural networks in eutrophication modeling. Eco Model. 2000; 134(2): 145-52
  • 34. Vekerdy Z, Lakatos L, Balla G, Oroszlan G. An international replication, and the need for long term follow up studies. Arch Dis Child Fetal Neonatal Ed. 2006; 91(6)[DOI][PubMed]
  • 35. MacLeod SL, McClure EL, Wong CS. Laboratory calibration and field deployment of the polar organic chemical integrative sampler for pharmaceuticals and personal care products in wastewater and surface water. Environ Toxicol Chem. 2007; 26(12): 2517-29[DOI][PubMed]
  • 36. Jang M, Lee HJ, Shim Y. Rapid removal of fine particles from mine water using sequential processes of coagulation and flocculation. Environ Technol. 2010; 31(4): 423-32[DOI][PubMed]
  • 37. Adomako D, Nyarko BJ, Dampare SB, Serfor-Armah Y, Osae S, Fianko JR, et al. Determination of toxic elements in waters and sediments from River Subin in the Ashanti Region of Ghana. Environ Monit Assess. 2008; 141(1-3): 165-75[DOI][PubMed]
  • 38. Hush DR, Horne BG. Progress in supervised neural networks. Signal Processing Magazine, IEEE. 1993; 10(1): 8-39
  • 39. Cheng J, Li Q, Xiao R. A new artificial neural network-based response surface method for structural reliability analysis. Probabilistic Engineering Mechanics. 2008; 23(1): 51-63
  • 40. Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw. 1994; 5(6): 989-93[DOI][PubMed]
Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:

Author(s):

Article(s):

Create Citiation Alert
via Google Reader

Readers' Comments