Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS), Inverse Distance Weighting and Geostatistics Methods for Estimating the Water Table (Case Study: Dehgolan Plain, Kurdistan Province)

Document Type : Research Article


1 Assistant Professor, Department of Earth Science, Faculty of Science, University of Kurdistan

2 M.Sc. of Hydrogeology, Department of Geology, Faculty of Sciences, Urmia University

3 Assistant Professor, Department of Geology, Faculty of Sciences, Urmia University


The decline of water table is very important in from a managerial point of view and might cause negative impacts such as land subsidence, raising costs and reducing groundwater quality. Groundwater is the most important source of water supply in Dehgolan plain. Increasing water requirements and extractions, has declined water table. This plain with an area of about 780 km2 is one of the protected plains of the Kurdistan province and with decrease in water table about 37 meters, it has the most decline between the plains of the province. The purpose of this study is to model the groundwater level and compare the performance of the method of Adaptive Neuro-Fuzzy Inference System (ANFIS) with Inverse Distance Weighted (IDW), Kriging and Cokriging methods. For this purpose, in September 2016, the water table data relating to the 44 Piezometer digged in Dehgolan plain has been used for modeling. The results show that the hydraulic head behavior is different across the aquifer, so the use of spatial data (h) for modeling doesn’t lead to satisfactory outputs. The water table in Dehgolan plain has the highest correlation with topography conditions and the ANFIS with a RMSE = 0.07, MSE = 0.005, MAE = 0.06, MBE = 0.04 and = 0.88 R2, has presented better performance than other methods.


Main Subjects

[1]. Todd DK, Mays LW. Groundwater Hydrology. 2nd Ed. New York: Wiley; 1980. 552 pp.
[2]. Khaledian F, Kalantari N, Javid A. Investigating the Construction's Effect of Sange Siah Reservoir Dam on Hydrogeology and Hydrogeochemistry of Dehgolan Plain Aquifer. M.Sc. Thesis, Faculty of Earth Sciences, Shahid Chamran University of Ahvaz; 2014. (In Persian)
[3]. Jahani N, Fathi P, Nasri B. Mathematical Modeling of Groundwater Resources System of Dehgolan plain. M.Sc. Thesis, Faculty of Agriculture, University of Kurdistan; 2009. (In Persian)
[4]. Ramezani E, Heidari A, Fathi P. Mathematical modeling of groundwater flow of Dehgolan Plain. 5th National conference on Water Resources Management, Shahid Beheshti University, Tehran; 2013. (In Persian)
[5]. Ebrahimi M, Fathi P. Predicting of Groundwater Level Fluctuation Using Artificial Neural Network and Nero – Fuzzy Interface System (case study: Dehgolan Aquifer). M.Sc. Thesis, Faculty of Agriculture, University of Kurdistan; 2011. (In Persian)
[6]. Dehghani R, Noorali A. Comparison of geostatistical methods and artificial neural network in estimating groundwater level (Case study: Nourabad plain, Lorestan). Journal of Environmental Science and Technology. 2016; 18(1): 33-44. (In Persian)
[7]. Zamaniahmadmahmoodi R, Akhondali A, Samadiborojeni H, Zareei H. Estimation of the groundwater level by using combined geostatistics with artificial neural networks (Case study: Shahrekord plain). Journal of Irrigation Science and Engineering. 2013; 36(1): 45-56. (In Persian)
[8]. Mokarram M, Mokarram MJ, Zarei AR, Safarinejadian B. Using adaptive Neuro-Fuzzy network (ANFIS) to predict underground water quality in west of Fars province during 2003 to 2013 period. Iranian Journal of Eco Hydrology. 2017; 4(2): 547-559. (In Persian)
[9]. Vadiati M, Nakhaei M. Groundwater quality evaluation of Tehran province for agricultural uses by fuzzy inference model. Water and Soil Science. 2015; 25(1): 41-52. (In Persian)
[10]. Tumez B, Hatipoglu Z. Comparing two data driven interpolation methods for modeling nitrate distribution in aquifer. Ecological Informatics. 2010; 5: 311-315.
[12]. Khoshravesh M, Gholami Sefidkouhia MA, Abbaspalangi J, Mirnaseric M. Estimation of nitrate concentrations in well and spring water using ANFIS and SVM models (Case study: Golestan province). Journal of Applied Hydrology. 2015; 2(2):1-12.
[13]. Jeihouni M, Delirhasannia R, Alavipanah SK, ShahabiM, Samadianfard S. Spatial analysis of groundwater electrical conductivity using ordinary kriging and artificial intelligence methods (Case study: Tabriz plain, Iran). Geofizika. 2015; 32(5): 191-208.
[14]. Yousefzadeh S, Nadiri AA. Comparison between ANN and ANFIS in hydraulic conductivity estimation of Maragheh-Bonab aquifer using geophysical data. International conference on Science and Engineering, Dubai, UAE; 2015. (In Persian)
[15]. Xiao Y, Gu X, Yin S, Shao J, Cui Y, Zhang Q, Niu Y. Analysis of groundwater level in piedmont plains, northwest China. Springer Plus. 2016; 5: 425.
[16]. Hassan I, Lawal IM, Mohammed A, Abubakar S. Analysis of geostatistical and deterministic techniques in the spatial variation of groundwater depth in the northwestern part of Bangladesh. American Journal of Engineering Research. 2016; 5(3): 29-34.
[17]. Talpur N, Mohd Salleh MN, Hussain K. An investigation of membership functions on performance of ANFIS for solving classification problems. International Research and Innovation Summit IOP Conf. Series: Materials Science and Engineering. 2017; Volume 226, conference 1.
[18]. Khosravi K, Habibnejad Roshan M, Solaimani K, Babaei K. Assessment of groundwater vulnerability using a-GIS based DRASTIC model (Case study: Dehgolan plain, Kurdistan province). Journal of Watershed Management Research. 2012; 3(5) :42-62. (In Persian)
[19]. Ebrahimi Mohammadi S, Azari M, Entezami. Investigating the quaternary deposits of Dehgolan Plain to determine prone areas to flood spreading. 1th national conference on Rainwater Catchment Systems, Islamic Azad University, Khomeini-Shahr; 2012. (In Persian)
[20]. Nadiri A, Naderi K, Asghari Moghaddam A, Habibi MH. Spatiotemporal predicting of groundwater level using artificial intelligence models and geostatistics model (Case study: Duzduzan plain). Journal of Geography and Planning. 2016; 20(58): 281-301. (In Persian)
[21]. Hasani Pak AA. Geostatistics. 5nd ed. Tehran: Tehran University Press; 2013. 328 pp. (In Persian)
[22]. Mohammadyari F, Aghdar A, Basiri R. Zoning groundwater quality for drinking using geo-statistical methods Case Study: Arid Regions in Mehran and Dehloran. Geographical Data. 2017; 26(101): 199-208.
[23]. Fathi Hafashjani E, Beigi Harchegani H, Davoudian Dehkordi AR, Tabatabaei S. Comparison of spatial interpolation methods and selecting the appropriate method for mapping of nitrate and phosphate in the Shahrekord aquifer. Iranian of Irrigation and water Engineering. 2014; 4(15): 51-63. (In Persian)
[24]. Haghizadeh A, Kiani A, Kiani M. Performance evaluation of geostatistical methods to estimate the spatial distribution of snow depth and density in mountainous areas (Case study: Gush Bala watershed, Mashhad). Hydrogeomorphology. 2017; 3(12): 45-66. (In Persian)
[25]. Khosravi Y, Abbasi E. Spatial analysis of environmental data using geostatistics. 1nd ed. Zanjan: Azarkelk Press; 2015. 282pp. (In Persian)
[26]. Jahanshahi A, Rohimogaddam E, Dehvari A. Investigating water quality parameters using GIS and geostatistics (Case study: Shahr-Babak plain aquifer).Water and Soil Science. 2014; 24(2): 183-197. (In Persian)
[27]. Moeeni H, Bonakdari H, Fatemi SE, Ebtehaj I. Modeling the monthly inflow to Jamishan dam reservoir using autoregressive integrated moving average and adaptive Neuro-Fuzzy inference system models. 2016; 26(1-2): 273-285. (In Persian)
[28]. Abareshi F, Meftah Helghi M, Sanikhani H, Dehghani AA. Comparison of three intelligence techniques for predicting water table depth fluctuations (Case study: Zarringol plain). Journal of water and soil conservation. 2014; 21(1): 163-180. (In Persian)
[29]. Kord M, Asghari- Moghaddam A. Evaluation of drinking water quality of Ardabil plain aquifer by cokriging and fuzzy logic. Journal of water and soil conservation. 2015; 21(5): 225-240. (In Persian)
[30]. Maroofpoor S, Fakheri- Fard A, Shiri, J. Development and combination of soft computing and geostatistical models in estimation of spatial distribution of groundwater level. Journal of Water and Soil Resources Conservation. 2016; 6(4): 17-28. (In Persian)
[31]. Mousavi S, Nourani V, Alami MT. Assessment of Chloride Concentration in Groundwater by Conjugation of Artificial Intelligence and Wavelet Transform Coherence Approaches. Modares Civil Engineering Journal. 2017; 17(6): 233-244. (In Persian)
[32]. Ghanbari N, Rangzan K, Kabolizade M, Moradi P. Improve the results of the DRASTIC model using artificial intelligence methods to assess groundwater vulnerability in Ramhormoz alluvial aquifer plain. Journal of water and soil conservation. 2017; 24(2): 45-65. (In Persian)
[33]. Faez K, Moradi MH, Eslami M. Introducing a general criterion for optimizing fuzzy clustering. The Second Conference on Machine Vision, Image Processing and Applications, Tehran; 2003. (In Persian)
[34]. Okwu M, Adetunji O. A comparative study of
artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models in distribution system with nondeterministic inputs. International Journal of Engineering Business Management. 2018; 10: 1–17.
[35]. Rajaee T, Nourani V, Pouraslan F. Groundwater level forecasting using wavelet and kriging. Journal of Hydraulic Structures. 2016; 2(2): 1-21.
Volume 6, Issue 1
April 2019
Pages 51-64
  • Receive Date: 21 June 2018
  • Revise Date: 05 October 2018
  • Accept Date: 05 October 2018
  • First Publish Date: 21 March 2019