Forecasting of heavy metals concentration in groundwater resources of Asadabad plain using artificial neural network approach

Document Type: Original Article


1 Department of Civil Engineering, Young Researchers and Elite Club, Hamadan Branch, Islamic Azad University, Hamadan, Iran

2 Department of the Environment, School of Basic Sciences, Hamadan Branch, Islamic Azad University, Hamadan, Iran


Nowadays 90% of the required water of Iran is secured with groundwater resources and forecasting of pollutants content in these resources is vital. Therefore, this research aimed to develop and employ the feedforward artificial neural network (ANN) to forecast the arsenic (As), lead (Pb), and zinc (Zn) concentration in groundwater resources of Asadabad plain. In this research, the ANN models were developed using MATLAB R2014 software program. The artificial intelligence models were trained with the data collected from field and then utilized as prediction tool. Levenberg-Marquardt (LM) and Bayesian regularization (BR) algorithms were employed as ANN training algorithms and their performance was evaluated using determination coefficient and the root mean square error. The results showed that the ANN models could potentially forecast heavy metals concentration in groundwater resources of the studied area. Coefficients of determination for ANN models for As, Pb and Zn in testing phase were 0.9288, 0.9823 and 0.8876, respectively. Finally, based on the simulation results, it was demonstrated that ANN could be applied effectively in forecasting the heavy metals concentration in groundwater resources of Asadabad plain. 


1. Gribble GW. The natural production of chlorinated compounds. Environ Sci Technol 1994; 28(7): 310A-9A.
2. Nasrabadi T. An IndexApproach to Metallic Pollution in RiverWaters. Int J Environ Res 2015; 9(1): 385-94.
3. Morillo J, Usero J, Gracia I. Partitioning of metals in sediments from the Odiel River (Spain). Environ Int 2002; 28(4): 263-71.
4. Baghvand A, Nasrabadi T, Bidhendi GN, Vosoogh A, Karbassi A, Mehrdadi N. Groundwater quality degradation of an aquifer in Iran central desert. Desalination 2010; 260(13): 264-75.
5. Hossein Pour M, Lashkaripour G, Dehghan P. Assessing the effect of heavy metal concentrations (Fe, Pb, Zn, Ni, Cd, As, Cu,Cr) on the quality of adjacent groundwater resources of Khorasan Steel Complex ). Int J Pl An and Env Sci 2014; 4(2): 511-8.
6. Prasanna MY, Chidambaram S, Hameed AS, Srinivasamoorthy K. Hydrogeochemical analysis and evaluation of groundwater quality in the Gadilam river basin, Tamil Nadu, India. J Earth Syst Sci 2011; 120(1): 85-98.
7. Prasad B, Kumari P, Bano S, Kumari S. Ground water quality evaluation near mining area and development of heavy metal pollution index. Appl Water Sci 2014; 4(1): 11-7.
8. Sobhanardakani S. Evaluation of the water quality pollution indices for groundwater resources of Ghahavand plain, Hamadan province, western Iran. Iran J Toxicol 2016; 10(3): 35-40.
9. Jarup L. Hazards of heavy metal contamination. Br Med Bull 2003; 68: 167-82.
10. Tahsin N, Yankov B. Research on accumulation of zinc (Zn) and cadmium (Cd) in sunflower oil. Journal of Tekirdag Agricultural Faculty 2007; 4(1): 109-11.
11. Sobhanardakani S, Jamshidi K. Assessment of Metals (Co, Ni, and Zn) Content in the Sediments of Mighan Wetland Using Geo-Accumulation Index. Iran J Toxicol 2015; 9(30): 1386-90.
12. Abou-Arab AAK, Ayesh AM, Amra HA, Naguib K. Characteristic levels of some pesticides and heavy metals in imported fish. Food Chem 1996; 57(4): 487-92.
13. Dahiya S, Karpe R, Hegde AG, Sharma RM. Lead, cadmium and nickel in chocolates and candies from suburban areas of Mumbai, India. J Food Comp Anal 2005; 18(6): 517-22.
14. Hosseini SV, Sobhanardakani S, Tahergorabi R, Delfieh P. Selected heavy metals analysis of Persian sturgeon's (Acipenser persicus) caviar from
Southern Caspian Sea. Biol Trace Elem Res 2013; 154(3): 357-62.
15. Adekunle IM, Akinyemi MF. Lead levels of certain consumer products in Nigeria: a case study of smoked fish foods from Abeokuta. Food Chem Toxicol 2004; 42(9): 1463-8.
16. Haykin SS. Neural Networks: A Comprehensive Foundation. 2nd ed. Upper Saddle River, NJ: Prentice Hall; 1999.
17. Nikolos IK, Stergiadi M, Papadopoulou MP, Karatzas GP. Artificial neural networks as an alternative approach to groundwater numerical modelling and environmental design. Hydrol Process 2008; 22(17): 3337-48.
18. Daliakopoulos IN, Coulibaly P, Tsanis IK. Groundwater level forecasting using artificial neural networks. J Hydrol 2005; 309(14):229 -40.
19. Antar MA, Elassiouti I, Allam MN. Rainfall-runoff modelling using artificial neural networks technique: a Blue Nile catchment case study. Hydrolog Process 2006; 20(5): 1201-16.
20. 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): 31-8.
21. Sobhanardakani S, Jamali M, Maanijou M. Evaluation of as, Zn, Cr and Mn concentrations in groundwater resources of razan plain and preparation of zoning map using gis. J Environ Sci Technol 2014; 16(2): 25-38.
22. Fazel Tavasol S, Vusuq BP, Manshuri M. The investigation of heavy metal (Sn-Pb) concentration in ground water resources and their environmental effects, Case study: North Chardoly Plain. Proceedings of the 1st International Applied Geological Congress; 2010 Apr 26-28; Mashhad, Iran.
23. Clesceri LS, Greenberg AE, Eaton AD. Standard methods for the examination of water and waste water, quality assurance. 20th ed. Washington DC: American Public Health Association; 1999.
24. Eaton AD, Franson MA. Standard methods for the examination of water & wastewater. Washington, DC: American Public Health Association; 2005.
25. Edet A, Offiong O. Evaluation of water quality pollution indices for heavy metal contamination monitoring. A study case from Akpabuyo-Odukpani area, Lower Cross River Basin (southeastern Nigeria). Geo J 2002; 57(4): 295-304.
26. Hornik K, Stinchcombe M, White H. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Net 1990; 3(5): 551-60.
27. Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Net 1989; 2(5): 359-66.
28. Aziz AR, Vincent Wong KF. A Neural-Network
ANN approach to forecast metal content
Alizamir and Sobhanardakani
J Adv Environ Health Res, Vol. 4, No. 2, Spring 2016 77
Approach to the Determination of Aquifer Parameters. Ground Water 1992; 30(2): 164-66.
29. ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. Artificial Neural Networks in Hydrology. I: Preliminary Concepts. J Hydrol Eng 2000; 5(2): 115-23.
30. Amin S, Farjoud Mr, Shabani A. Groundwater contamination by heavy metals in water resources of Shiraz area. Iran Agric Res 2011; 30(1-2): 21-32.
31. Taghipour H, Mosaferi M, Pourakbar M, Armanfar F. Heavy metals concentrations in groundwater used for irrigation. Health Promot Perspect 2012; 2(2): 205-10.
32. Ghosh A, Das P, Sinha K. Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN). Appl Water Sci (2015) 5: 191 2015; 5(2): 191-9.
33. Gnanasangeetha D, SaralaThambavani D. Modelling of As3+ adsorption from aqueous solution using Azadirachta indica by artificial neural network. Desalin Water Treat 2015; 56(7): 1839-54.
34. Yurtsever U, Yurtsever M, Sengil A, Yilmazçoban NK. Fast artificial neural network (FANN)
modeling of Cd(II) ions removal by valonia resin. Desalin Water Treat 2014; 56(1): 83-96.
35. Marquardt DW. An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J Soc Ind Appl Math 1963; 11(2): 431-41.
36. Levenberg K. A method for the solution of certain non-linear problems in least squares. Quart Appl Math 1944; 2(2): 164-8.
37. Sudheer KP, Gosain AK, Ramasastri KS. A data-driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 2002; 16(6): 1325-30.
38. Lin J, CHENG CT, Chau KW. Using Support Vector Machines for Long-Term Discharge Prediction. Hydrol Sci J 2006; 51(4): 599-612.
39. Keskin TE, Dügenci M, Kaçaroglu F. Prediction of water pollution sources using artificial neural networks in the study areas of Sivas, Karabük and Bartin (Turkey). Environ Earth Sci 2015; 73(9): 5333-47.
40. Nor AS, Faramarzi M, Yunus MA, Ibrahim S. Nitrate and sulfate estimations in water sources using a planar electromagnetic sensor array and artificial neural network method. IEEE Sens J 2015; 15(1): 497-504.