Advertisement

Evolving genetic programming and other AI-based models for estimating groundwater quality parameters of the Khezri plain, Eastern Iran

  • Ahmad AryafarEmail author
  • Vahid Khosravi
  • Hosniyeh Zarepourfard
  • Reza Rooki
Original Article
  • 23 Downloads

Abstract

Genetic programming (GP) was used to determine relationships between groundwater quality parameters including total hardness (TH), total dissolved solids (TDS) and electrical conductivity (EC) for 240 groundwater samples collected from 12 wells in the Khezri plain, eastern Iran. The artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods were also developed to further verify the estimation capability of GP by a comparison of estimated and observed values of chemical parameters. Values obtained from field observations and different models revealed the superiority of genetic programming, with values of R2 = 0.98, RMSE = 51.38 and MARE = 0.093, for TH, R2 = 0.99, RMSE = 121.35 and MARE = 0.041, for TDS and R2 = 0.97, RMSE = 96.39 and MARE = 0.067 for EC. Satisfactory performances were also produced by the ANN and ANFIS methods for the estimation of the intended water quality parameters. Genetic programming can be considered as a promising tool for automatic modeling of the hydrochemical parameters with the aim of environmental management and optimal use of groundwater resources.

Keywords

Hydrochemical parameters Genetic programming Artificial neural networks Adaptive neuro-fuzzy inference system (ANFIS) Khezri plain 

Notes

Acknowledgements

The Authors would like to express special thanks to the Regional Water Company of South Khorasan (RWCSK) for providing data. The financial support provided by University of Birjand is also appreciated.

References

  1. Abraham A (2005) Adaptation of fuzzy inference system using neural learning. In: Fuzzy systems engineering. Springer, Berlin, pp 53–83Google Scholar
  2. Armaghani DJ, Momeni E, Abad SVANK, Khandelwal M (2015) Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting. Environ Earth Sci 74:2845–2860CrossRefGoogle Scholar
  3. Azad A, Karami H, Farzin S, Saeedian A, Kashi H, Sayyahi F (2018) Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (Case study: Gorganrood River. KSCE J Civ Eng 22:2206–2213CrossRefGoogle Scholar
  4. Banerjee P, Singh V, Chatttopadhyay K, Chandra P, Singh B (2011) Artificial neural network model as a potential alternative for groundwater salinity forecasting. J Hydrol 398:212–220CrossRefGoogle Scholar
  5. Batayneh A et al (2013) Hydrochemical facies and ionic ratios of the coastal groundwater aquifer of Saudi Gulf of Aqaba: implication for seawater intrusion. J Coast Res 30:75–87CrossRefGoogle Scholar
  6. Bello O, Holzmann J, Yaqoob T, Teodoriu C (2015) Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J Artif Intell Soft Comput Res 5:121–139CrossRefGoogle Scholar
  7. Chadalawada J, Havlicek V, Babovic V (2017) A genetic programming approach to system identification of rainfall-runoff models. Water Resour Manage 31:3975–3992.  https://doi.org/10.1007/s11269-017-1719-1 CrossRefGoogle Scholar
  8. Coulibaly P, Anctil F, Aravena R, Bobée B (2001) Artificial neural network modeling of water table depth fluctuations. Water Resour Res 37:885–896CrossRefGoogle Scholar
  9. Csábrági A, Molnár S, Tanos P, Kovács J (2017) Application of artificial neural networks to the forecasting of dissolved oxygen content in the Hungarian section of the river. Danube Ecol Eng 100:63–72CrossRefGoogle Scholar
  10. Danandeh Mehr A, Nourani V (2018) Season algorithm-multigene genetic programming: a new approach for rainfall–runoff modelling. Water Resour Manage 32:2665–2679.  https://doi.org/10.1007/s11269-018-1951-3 CrossRefGoogle Scholar
  11. Datta B, Prakash O, Sreekanth J (2014) Application of genetic programming models incorporated in optimization models for contaminated groundwater systems management. In: Tantar AA et al. (eds) EVOLVE—a bridge between probability, set oriented numerics, and evolutionary computation V, Cham, 2014// 2014. Springer, Berlin, pp 183–199Google Scholar
  12. Dehghani A, Asgari M, Mosaedi A (2009) Comparison of geostatistics, artificial neural networks and adaptive neuro-fuzzy inference system approaches in groundwater level interpolation (case study: Ghazvin aquifer. J Agric Sci Nat Resour 16:517–528Google Scholar
  13. Diamantopoulou MJ, Papamichail DM, Antonopoulos VZ (2005) The use of a neural network technique for the prediction of water quality parameters. Oper Res Int J 5:115–125CrossRefGoogle Scholar
  14. Drecourt J-P (1999) Application of neural networks and genetic programming to rainfall runoff modeling. Water Resour Manag 13:219–231CrossRefGoogle Scholar
  15. Emigdio Z, Abatal M, Bassam A, Trujillo L, Juárez-Smith P, El Hamzaoui Y (2017) Modeling the adsorption of phenols and nitrophenols by activated carbon using genetic programming. J Clean Prod 161:860–870CrossRefGoogle Scholar
  16. Fallah-Mehdipour E, Haddad OB, Mariño M (2013) Prediction and simulation of monthly groundwater levels by genetic programming. J Hydro-Environ Res 7:253–260CrossRefGoogle Scholar
  17. Fallah-Mehdipour E, Haddad OB, Marino MA (2014) Genetic programming in groundwater modeling. J Hydrol Eng 19(12):04014031CrossRefGoogle Scholar
  18. Faucett L (1994) Fundamentals of neural networks Architecture, AlgorithmsGoogle Scholar
  19. Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional ground water levels affected by human activities. Groundwater 46:80–90Google Scholar
  20. Fijani E, Moghaddam AA, Tsai FT-C, Tayfur G (2017) Analysis and assessment of hydrochemical characteristics of Maragheh–Bonab Plain aquifer. Northwest Iran Water Resour Manage 31:765–780CrossRefGoogle Scholar
  21. Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3:95–99CrossRefGoogle Scholar
  22. Gong Y, Zhang Y, Lan S, Wang H (2016) A comparative study of artificial neural networks, support vector machines and adaptive neuro fuzzy inference system for forecasting groundwater levels near Lake Okeechobee. Florida Water Resour Manage 30:375–391CrossRefGoogle Scholar
  23. Hagan M, Demuth H, Beale M (1996) De Jes us Neural network design, 2nd edn. PWS Publishing Co Boston, MAGoogle Scholar
  24. Hem JD (1985) Study and interpretation of the chemical characteristics of natural water vol 2254. Department of the Interior, US Geological SurveyGoogle Scholar
  25. Heydari F, Saghafian B, Delavar M (2016) Coupled quantity-quality simulation-optimization model for conjunctive surface-groundwater use. Water Resour Manage 30:4381–4397CrossRefGoogle Scholar
  26. Hill DJ, Minsker BS, Valocchi AJ, Babovic V, Keijzer M (2007) Upscaling models of solute transport in porous media through genetic programming. J Hydroinf 9:251–266.  https://doi.org/10.2166/hydro.2007.028 CrossRefGoogle Scholar
  27. Hong Y-S, Rosen MR (2002) Identification of an urban fractured-rock aquifer dynamics using an evolutionary self-organizing modelling. J Hydrol 259:89–104.  https://doi.org/10.1016/S0022-1694(01)00587-X CrossRefGoogle Scholar
  28. Hong YST, White PA, Scott DM (2005) Automatic rainfall recharge model induction by evolutionary computational intelligence Water Resour Res 41Google Scholar
  29. Jang J-S (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685CrossRefGoogle Scholar
  30. Jang J-SR, Sun C-T, Mizutani E (1997) Neuro-fuzzy and soft computing; a computational approach to learning and machine intelligenceGoogle Scholar
  31. Kasiviswanathan K, Saravanan S, Balamurugan M, Saravanan K (2016a) Genetic programming based monthly groundwater level forecast models with uncertainty quantification. Model Earth Syst Environ 2:27CrossRefGoogle Scholar
  32. Kasiviswanathan KS, Saravanan S, Balamurugan M, Saravanan K (2016b) Genetic programming based monthly groundwater level forecast models with uncertainty quantification. Model Earth Syst Environ 2:27.  https://doi.org/10.1007/s40808-016-0083-0 CrossRefGoogle Scholar
  33. Kisi O, Shiri J (2011) Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction. Models Water Resour Manage 25:3135–3152.  https://doi.org/10.1007/s11269-011-9849-3 CrossRefGoogle Scholar
  34. Kisi O, Dailr AH, Cimen M, Shiri J (2012) Suspended sediment modeling using genetic programming and soft computing techniques. J Hydrol 450–451:48–58.  https://doi.org/10.1016/j.jhydrol.2012.05.031 CrossRefGoogle Scholar
  35. Koza J (1992) Genetic programming: on the programming of computers by means of natural selection. MIT Press, BradfordGoogle Scholar
  36. Latha PS, Rao KN (2012) An integrated approach to assess the quality of groundwater in a coastal aquifer of Andhra Pradesh, India. Environ Earth Sci 66:2143–2169CrossRefGoogle Scholar
  37. Lin C-T, Lee CG (1996) Neural fuzzy systems PTR Prentice HallGoogle Scholar
  38. Lohani A, Goel N, Bhatia K (2006) Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship. J Hydrol 331:146–160CrossRefGoogle Scholar
  39. Lohani A, Kumar R, Singh R (2012) Hydrological time series modeling: a comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques. J Hydrol 442:23–35CrossRefGoogle Scholar
  40. Maier HR, Dandy GC (1996) The use of artificial neural networks for the prediction of water quality parameters. Water Resour Res 32:1013–1022CrossRefGoogle Scholar
  41. Mandal S, Mahapatra SS, Adhikari S, Patel RK (2015) Modeling of arsenic (III) removal by evolutionary genetic programming and least square support vector machine. Models Environ Proc 2:145–172.  https://doi.org/10.1007/s40710-014-0050-6 CrossRefGoogle Scholar
  42. 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 1–7Google Scholar
  43. Moosavi V, Vafakhah M, Shirmohammadi B, Behnia N (2013) A wavelet-ANFIS hybrid model for groundwater level forecasting for different prediction periods. Water Resour Manage 27:1301–1321CrossRefGoogle Scholar
  44. Olyaie E, Banejad H, Chau K-W, Melesse AM (2015) A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States. Environ Monit Assess 187:189CrossRefGoogle Scholar
  45. Olyaie E, Abyaneh HZ, Mehr AD (2017) A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware river. Geosci Front 8(3):517–527CrossRefGoogle Scholar
  46. Prakash O, Datta B (2014) Multiobjective monitoring network design for efficient identification of unknown groundwater pollution sources incorporating genetic programming–based monitoring J Hydrol Eng.  https://doi.org/10.1061/(ASCE)HE.1943-5584.0000952 CrossRefGoogle Scholar
  47. Salami E, Ehteshami M (2015) Simulation, evaluation and prediction modeling of river water quality properties (case study: Ireland Rivers. Int J Environ Sci Technol 12:3235–3242CrossRefGoogle Scholar
  48. Sheikhy Narany T, Ramli MF, Aris AZ, Sulaiman WNA, Juahir H, Fakharian K (2014) Identification of the hydrogeochemical processes in groundwater using classic integrated geochemical methods and geostatistical techniques, in Amol-Babol plain, Iran The Scientific World Journal 2014Google Scholar
  49. Sreekanth J, Datta B (2010) Multi-objective management of saltwater intrusion in coastal aquifers using genetic programming and modular neural network based surrogate models. J Hydrol 393:245–256.  https://doi.org/10.1016/j.jhydrol.2010.08.023 CrossRefGoogle Scholar
  50. Sridharan M, Nathan DS (2017) Groundwater quality assessment for domestic and agriculture purposes in Puducherry region. Appl Water Sci 7:4037–4053CrossRefGoogle Scholar
  51. Srinivas R, Bhakar P, Singh AP (2015) Groundwater quality assessment in some selected area of Rajasthan, India using fuzzy multi-criteria decision making tool. Aquatic Proc 4:1023–1030CrossRefGoogle Scholar
  52. Srinivasamoorthy K, Vasanthavigar M, Vijayaraghavan K, Sarathidasan R, Gopinath S (2013) Hydrochemistry of groundwater in a coastal region of Cuddalore district, Tamil Nadu, India: implication for quality assessment Arabian. J Geosci 6:441–454Google Scholar
  53. Zadeh LA (1996) Fuzzy sets. In: Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A Zadeh. World Scientific, pp 394–432Google Scholar
  54. Zare M, Koch M (2018) Groundwater level fluctuations simulation and prediction by ANFIS-and hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) clustering models: application to the Miandarband plain. J Hydro Environ Res 18:63–76CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Ahmad Aryafar
    • 1
    Email author
  • Vahid Khosravi
    • 1
  • Hosniyeh Zarepourfard
    • 1
  • Reza Rooki
    • 2
  1. 1.Department of Mining, Faculty of EngineeringUniversity of BirjandBirjandIran
  2. 2.Department of Mining, Faculty of EngineeringBirjand University of TechnologyBirjandIran

Personalised recommendations