The modeling and prediction of the quality of the groundwater resources in Tuyserkan plain using the optimized artificial neural network

Document Type: Original Article


1 Department of Environmental Sciences, Faculty of Natural Resources and Environment, Malayer University, Malayer, Hamedan, Iran

2 Environmental Health Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran



Tuyserkan plain is an important agricultural plain located in Hamadan province, Iran. Despite the severe decline of the water levels in aquifers, the quality of the plain has not been evaluated in recent years. The present study aimed to analyze the data of 15 wells during 12 years to evaluate the quality of groundwater in this area using the Wilcox diagram for the aquifer. The electrical conductivity (EC) of the plain was interpolated using the Kriging method to evaluate its spatial distribution since this parameter has caused a decline in the quality of the groundwater in the plain. According to the findings, the EC value was higher in the eastern parts of the plain and Tuyserkan city, which was described as the spatial distribution of the parameter. The Pearson's correlation-coefficient was used to assess the correlations between EC and other parameters. To predict and model the EC value, multi-layer perceptron artificial neural network (MLP-ANN) were used. According to the results of the Pearson's correlation-coefficient, the reduced number of the data led to the decreased expenditures of the experiments in obtaining the input data. The third model was finally obtained with the lowest number of the input parameters, low error, and high correlations between the predicted and actual data. In this model, two input parameters and five neurons were obtained in a hidden layer (R: 0.997, mean: 8.634, NRMSE: 0.05) using the momentum and hyperbolic tangent functions, indicating the high potential of MLP-ANNs in the prediction and modeling of groundwater quality.


1. Taghizadeh Mehrjardi R, Zareian Jahromi M, Mahmodi Sh, Heidari A. Spatial distribution of groundwater quality with geostatistics (Case study: Yazd-Ardakan Plain). World Appl Sci J 2008; 4 (1): 09-17.

2. Parsimehr M, Shayesteh K, Godini K, Bayat Varkeshi M. Using multilayer perceptron artificial neural network for predicting and modeling the chemical oxygen demand of the Gamasiab River. Avicenna J Environ Health Eng 2018; 5(1): 15–20.

3. Salman SA, Shahid S, Mohsenipour M, Asgari H. Impact of landuse on groundwater quality of Bangladesh. Sustain Water Resour Manag 2018; 4(4): 1031–6.

4. Lutz A, Thomas JM, Keita M. Effects of population growth and climate variability on sustainable groundwater in Mali, West Africa. Sustainability 2011; 3(1): 21–34.

5. Shah T, Roy AD, Qureshi AS, Wang J. Sustaining Asia’s groundwater boom: An overview of issues and evidence. Natural Resources Forum 2003; 27(2): 130-41.

6. Tahmasebi AR, Zomorrodian SMA. Estimation of soil liquefaction potential using artificial neural network. Second National Student Conference on Water and Soil Resources 2004.  (In Persian)

7. Sarkar A, Pandey P. River water quality modelling using artificial neural network technique. Aquat Procedia 2015; 4: 1070–7.

8. Jalalkamali A, Jalalkamali N. Adaptive network-based fuzzy inference system-genetic algorithm models for prediction groundwater quality indices: A GIS-based analysis. Journal of AI and Data Mining 2018; 6(2): 439–45.

9. Alizamir M, Sobhanardakani S. Forecasting of heavy metals concentration in groundwater resources of Asadabad Plain using artificial neural network approach. J Adv Environ Health Res 2016; 4(2): 68–77.

10. Azimi S. Azhdary Moghaddam M. Hashemi Monfared SA. Prediction of annual drinking water quality reduction based on groundwater resource index using the artificial neural network and fuzzy clustering. J Contam Hydrol 2019; 220: 6–17.

11. Soltani Mohammadi A, Sayadi Shahraki A, Naseri AA. Simulation of groundwater quality parameters using ANN and ANN+ PSO Models (Case Study: Ramhormoz Plain). Pollution 2017; 3(2): 191–200.

12. Mehrdadi N, Hasanlou H, Jafarzadeh MT, Hasanlou H, Abdolabadi H. Simulation of low TDS and biological units of Fajr industrial wastewater treatment plant using artificial neural network and principal component analysis hybrid method. J Water Resource Prot 2012; 4(6): 370-6.

13. Wilcox Lv. The quality of water for irrigation use. 1948.

14.  Webster R, Oliver Ma. Geostatistics for environmental scientists. John Wiley & Sons; 2007.

15. Mabit L, Bernard C. Assessment of spatial distribution of fallout radionuclides through geostatistics concept.  J Environ Radioact 2007; 97(2-3): 206–19.

16. Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H. Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-Rood River, Isfahan, Iran. Ksce Journal of Civil Engineering 2017; 21(1): 134–40.

17. Braddock RD, Kremmer ML, Sanzogni L. Feed‐Forward artificial neural network model for forecasting rainfall run‐off. Environmetrics 1998; 9(4): 419–32.

18. Asadpour G, Nasrabadi T. Municipal and medical solid waste management in different districts of Tehran, Iran. Fresenius Environ Bull 2011; 20(12): 3241–5.

19. Zare Abyaneh H, Bayat Varkeshi M, Maroufi S, Amiri Chaijan R. Evaluation of artificial neural network and adaptive neuro fuzzy inference system in decreasing of reference evapotranspiration parameters. Journal of Water and Soil (Agricultural Science and Technology) 2010; 24(2): 297-305. (In Persian)