Arabian Journal of Geosciences

, Volume 8, Issue 2, pp 903–912 | Cite as

Evaluation of ANFIS, ANN, and geostatistical models to spatial distribution of groundwater quality (case study: Mashhad plain in Iran)

Original Paper
First Online:
Received:
Accepted:

Abstract

Groundwater is one of the major sources of exploitation in arid and semiarid regions. Spatial and temporal quality distribution is an important factor in groundwater management. Thus for protecting groundwater quality, data on spatial and temporal distribution are important. Geostatistical models are the most advanced techniques for interpolation and spatial prediction of groundwater parameters. Determining the best and the most suitable model is also very essential which is the main aim in this study. In this research, inverse distance weighted (IDW), kriging, and cokriging methods in geostatistical and artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) models were used for predicting the spatial distribution of groundwater electrical conductivity (EC) and those were compared together. EC and chloride (Cl−) are two important indicators for water quality assessment. Data were related to 120 wells in Mashhad plain (Iran). Groundwater resources have an important role in this region due to surface water deficit. After normalization of data, to geostatistical models, variograme was drawn; for selecting a suitable model for fitness on experimental variograme, less RSS value was used. Then using cross-validation and root mean square error (RMSE), the best method for interpolation was selected. To compare these three models, we used 25 % of observation data and determined the R 2, RMSE, and MAE parameters. Different ANFIS structures were examined. Also in ANFIS method, different types of membership function, such as Gaussian, bell shape, and trapezoid for inputs of model, were used. Results showed that for interpolation of groundwater quality, cokriging method is superior to kriging method in geostatistical model. In cokriging method, Cl parameter was selected as auxiliary variable which had the highest correlation with EC. Results showed that ANN model had the best accuracy (R 2 = 0.932, RMSE = 367.9, MAE = 265.78 μmos/cm) than ANFIS and geostatistical models.

Keyword

Spatial distribution Geostatistical Artificial neural network (ANN) Adaptive neuro-fuzzy inference systems (ANFIS) Groundwater quality 
This is a preview of subscription content, log in to check access.

References

  1. Ahmed S (2002) Groundwater monitoring network design: application of geostatistics with a few case studies from a granitic aquifer in a semiarid region. In: Herif MM, Singh VP, Al-Rashed M (eds) Groundwater Hydrology, vol 2. Balkema, Tokyo, pp 37–57Google Scholar
  2. Barca E, Passarella G (2007) Spatial evaluation of the risk of groundwater quality degradation. A comparison between disjunctive kriging and geostatistical simulation, Environ Monit Assess 137(1–3):261–273Google Scholar
  3. Clair TA, Ehrman JM (1996) Variations in discharge and dissolved organic carbon and nitrogen export from terrestrial basins with changes in climate: a neural network approach. Limnol Oceanogr 41(5):921–927CrossRefGoogle Scholar
  4. Coulibaly P, Anctil F, Bobee B (1999) Provision hydrologique par reseaux de neurons artificials. Can J Civil Eng 26(3):293–304CrossRefGoogle Scholar
  5. Coulibaly P, Anctil FO, Aravena R, Bobe B (2001) Artificial neural network modeling of water table depth fluctuation. Water Resour Res 37(4):885–896CrossRefGoogle Scholar
  6. Daliakopoulosa IN, Coulibalya P, Tsanisb IK (2005) Groundwater level forecasting using artificial neural networks. J Hydrol 309:229–240CrossRefGoogle Scholar
  7. Dagostino V, Greene EA, Passarella G, Vurro M (1998) Spatial and temporal study of nitrate concentration in groundwater by means of co regionalization. Environ Geol 36:285–295CrossRefGoogle Scholar
  8. Doğan E (2008) Reference evapotranspiration estimation using adaptive neuro-fuzzy inference system. J Irrig Drain Eng ASCE 58:617–628Google Scholar
  9. Finke PA, Brus DJ, Finke PA, Brus DJ, Bierkens MFP, Hoogland T, Knotters M, De Vries F (2004) Mapping groundwater dynamics using multiple sources of exhaustive high resolution data. Geoderma 123:23–39CrossRefGoogle Scholar
  10. Gaus I, Kinniburgh DG, Talbot JC, Webster R (2003) Geostatistical analysis of rsenic concentration in groundwater in Bangladesh using disjunctive kriging. Environ Geol 44:939–948CrossRefGoogle Scholar
  11. Gundogdu KS, Guney I (2007) Spatial analyses of groundwater levels using universal kriging. J Earth Syst Sci 116(1):49–55CrossRefGoogle Scholar
  12. Iphar M (2012) ANN and ANFIS performance prediction models for hydraulic impact hammers. Tunn Undergr Space Technol 27(1):23–29CrossRefGoogle Scholar
  13. Istok JD, Cooper RM (1998) Geostatistics applied to groundwater pollution. III: global estimates. J Environ Eng 114(4):915–928CrossRefGoogle Scholar
  14. Kabiri-Samania AR, Aghaee-Tarazjanib J, Borgheic SM, Jengd DS (2011) Application of neural networks and fuzzy logic models to long-shore sediment Transport. Appl Soft Comput 11:2880–2887CrossRefGoogle Scholar
  15. Farahmand A, Manshouri M, Liaghat A, Sedghi H (2010) Comparison of kriging, ANN and ANFIS models for spatial and temporal distribution modeling of groundwater contaminants. J Food Agric Environ (JFAE) 8(3–4):1146–1155Google Scholar
  16. Karaca F, Ozkaya B (2006) A neural network-based model for controlling leachate flow-rate in a municipal solid waste landfill site. Environ Model Softw 21(8):1190–1197CrossRefGoogle Scholar
  17. Kiani S, Jafari A (2012) Crop detection and positioning in the field using discriminate analysis and neural networks based on shape features. J Agr Sci Tech 14:755–765Google Scholar
  18. Kisi O, Oztork (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng ASCE 133(4):368–379CrossRefGoogle Scholar
  19. Khashei-Siuki A, Kouchakzadeh M, Ghahraman B (2011) Predicting dryland wheat yield from meteorological data using expert system in Khorasan province, I.R. Iran. JAST 13:627–640Google Scholar
  20. Kholghi M, Hosseini SM (2009) Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environ Model Assess 14:729–737CrossRefGoogle Scholar
  21. Kumara MS, Bhat A (2007) Application of arc GIS geostatistical analyst for interpolating environmental data from observations. Environ Prog 26(3):220–355CrossRefGoogle Scholar
  22. Lee ES (2000) Neuro-fuzzy estimation in spatial statistics. J Math Anal Appl 249:221–231. doi: 10.1006/jmaa.2000.6938 CrossRefGoogle Scholar
  23. Lek S, Guiresse M, Giraudel JL (1999) Predicting stream nitrogen concentration from watershed features using neural networks. Water Res 33(16):3469–3478CrossRefGoogle Scholar
  24. Mercader S, Cabrera-Mercader CR, Staelin DH (1995) Passive microwave relative humidity retrievals using feed forward neural network. IEEE Trans Geosci Remote Sens 33(6):842–852Google Scholar
  25. Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32(4):1013–1022CrossRefGoogle Scholar
  26. Marsily GD, Ahmed S (1987) Application of kriging techniques in groundwater hydrology. J Geol Soc India 29(1):57–79Google Scholar
  27. Najafi MR, Lee KT, Hosseini SM (2007) ANN modeling for estimation of surface and subsurface flows based on watershed geomorphology. J Agric Sci Technol 9:305–316Google Scholar
  28. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) Aneuro-fuzzy computing technique for modeling hydrological time series. J Hydrol (Amsterdam) 291:52–66. doi: 10.1016/j.jhydrol.2003.12.010 CrossRefGoogle Scholar
  29. Nayak PC, Rao YRS, Sudheer KP (2006) Groundwater level forecasting in a shallow aquifer using artificial neural network approach. Water Resour Manag 20:77–90CrossRefGoogle Scholar
  30. Nazari Zade F, Arshadiyan B, Zand Vakily K (2006( Study of spatial variability of groundwater quality of Balarood Plain in Khuzestan province. The first congress of optimized exploitation from water source of Karoon and Zayanderood Plain. Shahre kord University, Persian Version, pp: 1236–1240Google Scholar
  31. Onkal-Engin G, Demir I, Engin SN (2005) Determination of the relationship between sewage odour and BOD by neural networks. Environ Model Softw 20(7):843–850CrossRefGoogle Scholar
  32. Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural networks. J Water Resour Plan Manag ASCE 122(5):342–347CrossRefGoogle Scholar
  33. Rezaei M, Davatgar N, Tajdari K, Abolpour B (2010) Investigation the spatial variability of some important groundwater quality factors in Guilan, Iran. J Water Soil 24(5):932–941, IN FARSIGoogle Scholar
  34. Rizzo DM, Mouser JM (2000) Evaluation of Geostatistics for Combined hydrochemistry and Microbial Community Fingerprinting at a Waste Disposal Site, pp: 1–11Google Scholar
  35. Robinson TP, Metternicht G (2006) Testing the performance of spatial interpolation techniques for mapping soil properties. Comput Electron Agric 50:97–108CrossRefGoogle Scholar
  36. Rodriguez MJ, Serodes JB (1998) Assessing empirical linear and nonlinear modelling of residual chlorine in urban drinking water systems. Environ Model Softw 14(1):93–102CrossRefGoogle Scholar
  37. Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481CrossRefGoogle Scholar
  38. Safari M )2002( Determination filtration network of Groundwater using geostatistic method. Sc Thesis. Tarbiyat Modares University Agricultural Faculty, Persian VersionGoogle Scholar
  39. Sahinkaya E, Muhsin N, Ozkaya B (2008) Neural network prediction of nitrate in groundwater of Harran Plain, Turkey M. Irfan Yesilnacar. Environ Geol 56:19–25CrossRefGoogle Scholar
  40. Sahinkaya E, O’zkaya B, Kaksonen AH, Puhakka JA (2007) Neural network prediction of thermophilic (65_C) sulfidogenic fluidized-bed reactor performance for the treatment of metal containing wastewater. Biotechnol Bioeng 97(4):780–787CrossRefGoogle Scholar
  41. Setnes M, Babuska R, Verbruggen HB (1998) Transparent fuzzy modeling. Intern J Human–Comput Stud 49:159–179. doi: 10.1006/ijhc.1998.0197 CrossRefGoogle Scholar
  42. Talebizadeh M, Moridnejad A (2011) Uncertainty analysis for the forecast of lake level fluctuations using ensembles of ANN and ANFIS models. Expert Syst Appl 38(4):4126–4135CrossRefGoogle Scholar
  43. Taghizadeh M, Zareian Jahromi M, Mahmodi SH, Heidari A (2008) Spatial distribution of groundwater quality with geostatistics (case study: Yazd-Ardakan plain). World Appl Sci J 4(1):09–17Google Scholar
  44. Todd DK (1980) Groundwater hydrology. John Wiley and Sons, New YorkGoogle Scholar
  45. Tutmez B, Hatipoglu Z, Kaymak U (2006) Modelling electrical conductivity of groundwater using an adaptive neurofuzzy inference system. Comput Geosci 32:421–433. doi: 10.1016/j.cageo.2005.07.003 CrossRefGoogle Scholar
  46. Wen CW, Lee CS (1998) A neural network approach to multiobjective optimization for water quality management in a river basin. Water Resour Res 34(3):427–436CrossRefGoogle Scholar
  47. Yazdani MR, Saghafian B, Mahdian MH, Soltani S (2009) Monthly runoff estimation using artificial neural networks. J Agric Sci Technol 11:355–362Google Scholar
  48. Yesilnacar MI, Sahinkaya E, Naz M, Ozkaya B )2007) Neural network prediction of nitrate in groundwater of Harran Plain, Turke. Journal of Environmental Geology, Publisher: Springer Berlin / Heidelberg, ISSN: 0943–0105 (Print) 1432–0495 (Online)Google Scholar
  49. Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958–5966CrossRefGoogle Scholar
  50. Zounemat-Kermani M, Teshnehlab M (2008) Using adaptive neuro-fuzzy inference system for hydrological time series prediction. Appl Soft Comput 8:928–936CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2013

Authors and Affiliations

  1. 1.Water Engineering DepartmentUniversity of Birjand of IranBirjandIran
  2. 2.Kavosh Water and Soil Research CenterMashhadIran

Personalised recommendations