Abstract
Simulation of groundwater quality is important for managing water resources and mitigating water shortages, especially in arid and semiarid areas. Geostatistical models have been used for spatial prediction and interpolation of groundwater parameters. Recently, hybrid intelligent models have been employed for the simulation of dynamic systems. In this study, hybrid intelligent models, based on a neuro-fuzzy system integrated with fuzzy c-means data clustering (FCM) and grid partition (GP) models as well as artificial neural networks integrated with particle swarm optimization algorithm, were used to predict the spatial distribution of chlorine (Cl), electrical conductivity (EC), and sodium absorption ratio (SAR) parameters of groundwater. Results of the hybrid models were compared with geostatistical methods, including kriging, inverse distance weighting (IDW), and radial basis function (RBF). The latitude and longitude values of observation wells and qualitative parameters in three states of maximum, average, and minimum were introduced as input and output to the models, respectively. To evaluate the models, the root mean squared error (RMSE), the mean absolute error (MAE), and CC statistical criteria were used. Results showed that in the hybrid models, NF-GP with the lowest RMSE and MAE and highest CC was the most suitable model for the prediction of water quality parameters. The RMSE, MAE, and CC values were 107.175 (mg/L), 79.804 (mg/L), and 0.924 in the average state for Cl; were 518.544 (μmho/cm), 444.152 (μmho/cm), and 0.882 for electrical conductivity; and were 1.596, 1.350, and 0.582 for sodium absorption ratio, respectively. Among the geostatistical models, the kriging was found more accurate. Using the coordinates of wells will eventually allow the NF-GP to be used for more sampling and replace the visual techniques that require more time, cost, and facilities.
Similar content being viewed by others
References
Abonyi J, Andersen H, Nagy L, Szeifert F (1999) Inverse fuzzy-process-model based direct adaptive control. Math Comput Simul 51:119–132
Adamowski J, Fung Chan H, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48
Adnan RM, Liang Z, Trajkovic S, Zounemat-Kermani M, Li B, Kisi O (2019a) Daily streamflow prediction using optimally pruned extreme learning machine. J Hydrol 577:123981
Adnan RM, Malik A, Kumar A, Parmar KS, Kisi O (2019b) Pan evaporation modeling by three different neuro-fuzzy intelligent systems using climatic inputs. Arab J Geosci 12:606
Aguilar FJ, Agüera F, Aguilar MA, Carvajal F (2005) Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogramm Eng Remote Sens 71:805–816
Almasri MN, Kaluarachchi JJ (2005) Modular neural networks to predict the nitrate distribution in ground water using the on-ground nitrogen loading and recharge data. Environ Model Softw 20:851–871
Apaydin H, Sonmez FK, Yildirim YE (2004) Spatial interpolation techniques for climate data in the GAP region in Turkey. Clim Res 28:31–40
Arslan H (2012) Spatial and temporal mapping of groundwater salinity using ordinary kriging and indicator kriging: the case of Bafra Plain, Turkey. Agric Water Manag 113:57–63
Azad N, Behmanesh J, Rezaverdinejad V, Abbasi F, Navabian M (2018) Developing an optimization model in drip fertigation management to consider environmental issues and supply plant requirements. Agric Water Manag 208:344–356
Belkhiri L, Mouni L, Tiri A, Narany TS, Nouibet R (2018) Spatial analysis of groundwater quality using self-organizing maps. Groundw Sustain Dev 7:121–132
Bezdek JC (1973): Cluster Validity with Fuzzy Sets. J Cybernetics 3:58–73
Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398:292–302
Coulibaly P, Anctil F, Bobée B (1999) Prévision hydrologique par réseaux de neurones artificiels: état de l'art. Can J Civ Eng 26:293–304
Diop L, Bodian A, Djaman K, Yaseen ZM, Deo RC, El-Shafie A, Brown LC (2018) The influence of climatic inputs on stream-flow pattern forecasting: case study of upper Senegal River. Environ Earth Sci 77:182
Elzwayie A, El-Shafie A, Yaseen ZM, Afan HA, Allawi MF (2017) RBFNN-based model for heavy metal prediction for different climatic and pollution conditions. Neural Comput & Applic 28:1991–2003
Emamgholizadeh S, Kashi H, Marofpoor I, Zalaghi E (2014) Prediction of water quality parameters of Karoon River (Iran) by artificial intelligence-based models. Int J Environ Sci Technol 11:645–656
Emamgholizadeh S, Bahman K, Bateni SM, Ghorbani H, Marofpoor I, Nielson JR (2017) Estimation of soil dispersivity using soft computing approaches. Neural Comput & Applic 28:207–216
Gao C, Gemmer M, Zeng X, Liu B, Su B, Wen Y (2010) Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network. Stoch Env Res Risk A 24:685–697
Gazzaz NM, Yusoff MK, Aris AZ, Juahir H, Ramli MF (2012) Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors. Mar Pollut Bull 64:2409–2420
Geethanjali M, Slochanal SMR, Bhavani R (2008) PSO trained ANN-based differential protection scheme for power transformers. Neurocomputing 71:904–918
Ghavidel SZZ, Montaseri M (2014) Application of different data-driven methods for the prediction of total dissolved solids in the Zarinehroud basin. Stoch Env Res Risk A 28:2101–2118
Hu K, Huang Y, Li H, Li B, Chen D, White RE (2005) Spatial variability of shallow groundwater level, electrical conductivity and nitrate concentration, and risk assessment of nitrate contamination in North China Plain. Environ Int 31:896–903
Jalalkamali A (2015) Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Sci Inf 8:885–894
Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE T SYST MAN CY B 23:665–685
Jin T, Cai S, Jiang D, Liu J (2019) A data-driven model for real-time water quality prediction and early warning by an integration method. Environ Sci Pollut Res 26:30374–30385
Karterakis SM, Karatzas GP, Nikolos IK, Papadopoulou MP (2007) Application of linear programming and differential evolutionary optimization methodologies for the solution of coastal subsurface water management problems subject to environmental criteria. J Hydrol 342:270–282
Kennedy J, Eberhart R (1995) Particle swarm optimization (PSO), Proc. IEEE International Conference on Neural Networks, Perth, Australia, pp 1942–1948
Khalil B, Ouarda T, St-Hilaire A (2011) Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis. J Hydrol 405:277–287
Kholghi M, Hosseini S (2009) Comparison of groundwater level estimation using neuro-fuzzy and ordinary kriging. Environ Model Assess 14:729–737
Kisi O, Sanikhani H (2015a) Modelling long-term monthly temperatures by several data-driven methods using geographical inputs. Int J Climatol 35:3834–3846
Kisi O, Sanikhani H (2015b) Prediction of long-term monthly precipitation using several soft computing methods without climatic data. Int J Climatol 35:4139–4150
Kisi O, Yaseen ZM (2019) The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction. CATENA 174:11–23
Kisi O, Zounemat-Kermani M (2014) Comparison of two different adaptive neuro-fuzzy inference systems in modelling daily reference evapotranspiration. Water Resour Manag 28:2655–2675
Kisi O, Zounemat-Kermani M (2016) Suspended sediment modeling using neuro-fuzzy embedded fuzzy c-means clustering technique. Water Resour Manag 30:3979–3994
Kisi O, Sanikhani H, Zounemat-Kermani M, Niazi F (2015) Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data. Comput Electron Agric 115:66–77
Kisi O, Keshavarzi A, Shiri J, Zounemat-Kermani M, Omran E-SE (2017) Groundwater quality modeling using neuro-particle swarm optimization and neuro-differential evolution techniques. Hydrol Res 48:1508–1519
Kördel W, Garelick H, Gawlik BM, Kandile NG, Peijnenburg WJ, Rüdel H (2013) Substance-related environmental monitoring strategies regarding soil, groundwater and surface water—an overview. Environ Sci Pollut Res 20:2810–2827
Kumar M, Raghuwanshi N, Singh R, Wallender W, Pruitt W (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233
Liu M, Lu J (2014) Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river? Environ Sci Pollut Res 21:11036–11053
Liu B, Wang L, Jin Y-H (2007) An effective PSO-based memetic algorithm for flow shop scheduling. IEEE Trans Syst Man B Cybern 37:18–27
Luo D, Guo Q, Wang X (2003) Simulation and prediction of underground water dynamics based on RBF neural network. Acta Geosci Sin 24:475–478
Mantoglou A, Papantoniou M, Giannoulopoulos P (2004) Management of coastal aquifers based on nonlinear optimization and evolutionary algorithms. J Hydrol 297:209–228
Maroufpoor S, Fakheri-Fard A, Shiri J (2017) Study of the spatial distribution of groundwater quality using soft computing and geostatistical models. ISH J Hydraul Eng 25:232–238
Maroufpoor E, Sanikhani H, Emamgholizadeh S, Kişi Ö (2018) Estimation of wind drift and evaporation losses from sprinkler irrigation systems by different data-driven methods. Irrig Drain 67:222–232
Maroufpoor S, Maroufpoor E, Bozorg-Haddad O, Shiri J, Yaseen ZM (2019a) Soil moisture simulation using hybrid artificial intelligent model: hybridization of adaptive neuro fuzzy inference system with grey wolf optimizer algorithm. J Hydrol 575:544–556
Maroufpoor S, Sanikhani H, Kisi O, Deo RC, Yaseen ZM (2019b) Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches. Int J Climatol 39:3543–3557
Maroufpoor S, Shiri J, Maroufpoor E (2019c) Modeling the sprinkler water distribution uniformity by data-driven methods based on effective variables. Agric Water Manag 215:63–73
Maroufpoor S, Bozorg-Haddad O, Chu X (2020) Geostatistics: principles and methods. In: Pijush S, Dieu TB, Ravinesh CD, Subrata C (eds) Handbook of Probabilistic Models. Elsevier, Amsterdam, pp 229–242
Mehdizadeh S, Behmanesh J, Khalili K (2017) A comparison of monthly precipitation point estimates at 6 locations in Iran using integration of soft computing methods and GARCH time series model. J Hydrol 554:721–742
Melin P, Olivas F, Castillo O, Valdez F, Soria J, Valdez M (2013) Optimal design of fuzzy classification systems using PSO with dynamic parameter adaptation through fuzzy logic. Expert Syst Appl 40:3196–3206
Nas B (2009) Geostatistical approach to assessment of spatial distribution of groundwater quality. Pol J Environ Stud 18:1073–1082
Niroobakhsh M, Musavi-Jahromi S, Manshouri M, Sedghi H (2012) Prediction of water quality parameter in Jajrood River basin: application of multi layer perceptron (MLP) perceptron and radial basis function networks of artificial neural networks (ANNs). Afr J Agric Res 7:4131–4139
Nourani V, Khanghah TR, Sayyadi M (2013) Application of the artificial neural network to monitor the quality of treated water. J Inf Technol Manag 3:39–45
Palani S, Liong S-Y, Tkalich P (2008) An ANN application for water quality forecasting. Mar Pollut Bull 56:1586–1597
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1:33–57
Rostami AA, Isazadeh M, Shahabi M, Nozari H (2019) Evaluation of geostatistical techniques and their hybrid in modelling of groundwater quality index in the Marand Plain in Iran. Environ Sci Pollut Res 26:34993–35009
Sahinkaya E, Muhsin N, Ozkaya B, Yesilnacar MI (2008) Neural network prediction of nitrate in groundwater of Harran Plain, Turkey. J Environ Geol 56:19–25
Sanikhani H, Kisi O (2012) River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches. Water Resour Manag 26:1715–1729
Sanikhani H, Kisi O, Maroufpoor E, Yaseen ZM (2019) Temperature-based modeling of reference evapotranspiration using several artificial intelligence models: application of different modeling scenarios. Theor Appl Climatol 135:449–462
Selakov A, Cvijetinović D, Milović L, Mellon S, Bekut D (2014) Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank. Appl Soft Comput 16:80–88
Seyedzadeh A, Maroufpoor S, Maroufpoor E, Shiri J, Bozorg-Haddad O, Gavazi F (2020) Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure. Agric Water Manag 228:105905
Soltani Mohammadi A, Sayadi Shahraki A, Naseri AA (2017) Simulation of groundwater quality parameters using ANN and ANN+ PSO models (Case study: Ramhormoz Plain). Pollution 3:191–200
Subramani T, Elango L, Damodarasamy S (2005) Groundwater quality and its suitability for drinking and agricultural use in Chithar River basin, Tamil Nadu, India. Environ Geol 47:1099–1110
Sudheer K, Gosain A, Ramasastri K (2003) Estimating actual evapotranspiration from limited climatic data using neural computing technique. J Irrig Drain Eng 129:214–218
Tanaka K (1997) An Introduction to Fuzzy Logic for Practical Applications. Springer, 154 pp
Tiwari MK, Adamowski J (2013) Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour Res 49:6486–6507
Tung TM, Yaseen ZM (2020) A survey on river water quality modelling using artificial intelligence models: 2000-2020. J Hydrol 585:124670
Vaheddoost B, Guan Y, Mohammadi B (2020) Application of hybrid ANN-whale optimization model in evaluation of the field capacity and the permanent wilting point of the soils. Environ Sci Pollut Res 27:13131–13141
Varouchakis EA, Kolosionis K, Karatzas GP (2016) Spatial variability estimation and risk assessment of the aquifer level at sparsely gauged basins using geostatistical methodologies. Earth Sci Inf 9:437–448
Wang L-X (1997) A course in fuzzy systems and control, 2. Prentice Hall PTR, Upper Saddle River
Wang L, Li X, Cui W (2012) Fuzzy neural networks enhanced evaluation of wetland surface water quality. Int J Comput Appl Technol 44:235–240
WHO (1984) Guidelines for drinking water quality. World Health Organization, Geneva, p 130
Wilding L (1985) Spatial variability: its documentation, accommodation and implication to soil surveys, Soil spatial variability. Workshop (Las Vegas NV 1985-11-30). Pudoc, Wageningen, pp. 166–194
Yaseen ZM, Ramal MM, Diop L, Jaafar O, Demir V, Kisi O (2018) Hybrid adaptive neuro-fuzzy models for water quality index estimation. Water Resour Manag 32:2227–2245
Yaseen ZM, Ebtehaj I, Kim S, Sanikhani H, Asadi H, Ghareb MI, Bonakdari H, Mohtar W, Melini WH, Al-Ansari N (2019) Novel hybrid data-intelligence model for forecasting monthly rainfall with uncertainty analysis. Water 11:502
Yavari S, Maroufpoor S, Shiri J (2018) Modeling soil erosion by data-driven methods using limited input variables. Hydrol Res 49:1349–1362
Yesilnacar MI, Sahinkaya E (2012) Artificial neural network prediction of sulfate and SAR in an unconfined aquifer in southeastern Turkey. Environ Earth Sci 67:1111–1119
Zhou Z, Zhang G, Yan M, Wang J (2012) Spatial variability of the shallow groundwater level and its chemistry characteristics in the low plain around the Bohai Sea, North China. Environ Monit Assess 184:3697–3710
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Responsible editor: Xianliang Yi
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Maroufpoor, S., Jalali, M., Nikmehr, S. et al. Modeling groundwater quality by using hybrid intelligent and geostatistical methods. Environ Sci Pollut Res 27, 28183–28197 (2020). https://doi.org/10.1007/s11356-020-09188-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-020-09188-z