Presentating a New Approach for Evaluating the Hydro-geochemical Quality of Groundwater using Swarm Intelligence Algorithms

Document Type : Research Article


1 Assistant Professor of Computer Engineering, Faculty of Engineering, University of Bonab, Bonab, Iran

2 Ph.D Student, Department of Water Science and Engineering, University of Tabriz, Tabriz, Iran


In recent years, increasing salinity and reduction in the quality of groundwater have become one of the environmental challenges due to the penetration and mixing of pollutants. These challenges have created serious risks for the development of human societies and health. In order to prevent future risks and appropriate planning for preserving water resources, a qualitative study of groundwater resources is an essential requirement. In this research, a swarm intelligence approach based on election algorithm and gray wolf optimization algorithm is presented to determine the optimal values ​​for water quality parameters such as TDS, EC and SAR. In order to evaluate the proposed method, data on the plain of Bostanabad city in the 10 years period (2003-2013) were used and the results were evaluated based on Wilcox, Schuler and Piper measures. The results of the experiments show that the underground water of Bostanabad city is modest to acceptable for agriculture and drinking and is suitable for industry due to corrosion and hardness. Most of the data were in the C2S2 class, which is suitable for agriculture. The correlation coefficient above 95% indicates the acceptable accuracy of the gray wolf optimization algorithm in comparison with the election algorithm in estimating the groundwater quality parameters.


Main Subjects

[1]. Ahmadi SH, Sedghamiz A. Application and evaluation of kriging and cokiriging metods on groundwater depth mapping. Environ. Moint. Assess. 2008; 138: 357- 368. [Persian]
[2]. Samin M, Soltani J, Zeraatcar Z, Moasheri SA, Sarani N. Spatial Estimation of Groundwater Quality Parameters Based on Water Salinity Data using Kriging and Cokriging Methods. International Conference on Transport, Environment and Civil Engineering. 2012; 5 p. [Persian]
[3]. Mohammadzadeh Romiani H, Masoumi F. Optimization of concrete gravity dams dimensions using imperialist competitive algorithm (ICA). The 16th Hydrualic Conference of Iran, Faculty of Engineering, University of Mohaghegh Ardebili, Ardebil, 15-16 september. 2017. [Persian]
[4]. Khamr Z, Mahmoodi-Gharaie MH, Omrani S, Sayareh AR. Evaluation of water quality in the Zar mountain of west of Torbat-Heydarieh. Iranian Economic Geology Society Conference. 2012. [Persian]
[5]. Emami S, Hemmati M, Arvanaghi H. Performance evaluation of Imperialist Competitive and Genetic algorithm for estimating groundwater quality parameters (case study: Bostanabad plain). Hydrogeology. 2017; 2(2): 44-53. [Persian]
[6]. Vahabzadeh Gh, Delavar H, Ghorbani J, Eshrafi MR. Investigation of changes in Chlorine and Salinity levels of groundwater in Firoozabad plain and comparative evaluation of agricultural and drinkable water. Journal of Research in Environmental Health. 2018; 4(1): 67-74.
[7]. Zareh-Abianeh H, Bayat-Vorkeshi M, Akhavan S, Mohammadi M. Estimation of groundwater nitrate in Hamedan-Bahar plain using artificial neural network and data separation effect on prediction precision. Ecology. 2011; 37(58): 129-140.
[8]. Rafati L, Mokhtari M, Fazelinia F, Momtaz SM Mahvi AH. Evaluation of ground water fluoride concentration in Hamadan Province west of IRAN. Iranian Journal of Health Sciences. 2013; 1(3): 71-76. [Persian]
[9]. Moasheri SA, Rezapour OM, Beyranvand Z, Poornoori Z. Estimating the spatial distribution of groundwater quality parameters of Kashan plain with integration method of Geostatistics - Artificial Neural Network Optimized by Genetic-Algorithm. International Journal of Agriculture and Crop Sciences. 2013; 23: 2434-2442.
[10]. [Last Access: 2018/6/22]
 [11]. Mirjalili S, Mirjalili SM, Lewis A. Grey Wolf Optimizer, Advances in Engineering Software. 2015; 69: 920140 46-61.
[12]. Mech LD. Alpha Status, Dominance, and Division of Labor in Wolf Packs, Canadian Journal of Zoology. 1999; 77(8): 1196-1203.
[13]. Muro C, Escobedo R, Spector L, Coppinger R. Wolf-pack (Canis Lupus) Hunting Strategies Emerge from Simple Rules in Computational Simulations, Behavioural Processes. 2011; 88(3): 192-197.
[14]. Emami H, Derakhshan F. Election algorithm: A new socio-politically inspired strategy. AI Communications. 2015; 28: 591-603.
[15]. Eslamian, SS, Lavaei N. Modeling Nitrat pollution of Groundwater using Artificial Neural Network and Genetic Algoritm in an Arid zone, international Jornal of water, Special Issue on Groundwater and surface water Interaction (GSWI). 2009; 5(2): 194-203.
[16]. Larose DT. Discovering knowledge in data: an introduction to data mining. Jhon Wiley & Sons Inc. 2005; 240 p.
Volume 6, Issue 1
April 2019
Pages 177-190
  • Receive Date: 23 August 2018
  • Revise Date: 03 January 2019
  • Accept Date: 03 January 2019
  • First Publish Date: 21 March 2019
  • Publish Date: 21 March 2019