Advertisement

Soft Computing

, Volume 23, Issue 23, pp 12897–12910 | Cite as

Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques

  • Parveen SihagEmail author
  • Fatemeh Esmaeilbeiki
  • Balraj Singh
  • Isa Ebtehaj
  • Hossein Bonakdari
Methodologies and Application

Abstract

Accurate prediction of the unsaturated hydraulic conductivity (K) is necessary to check the feasibility of the artificial and natural groundwater recharge. In this study, one artificial intelligence (AI), i.e., adaptive neuro-fuzzy inference system (ANFIS) technique, and two hybrid techniques (combination of traditional AI + optimization technique), i.e., ANFIS + firefly algorithms (ANFIS-FFA) and ANFIS + particle swarm optimization (ANFIS-PSO), are used to predict the K of the soil. The study area for this investigation is Ghaggar basin. For the present study, dataset (240 observations) was collected from field experiments using minidisk infiltrometer. Total dataset was segregated into two different parts. Larger part (170 data) was used for model development, and smaller part (70 data) was used to check the performance of developed models. Four popular statistical parameters were used to evaluate the performance of developed models. Results indicate that the performance of ANFIS-PSO and ANFIS-FFA was comparable with higher accuracy in prediction of K of the soil than traditional ANFIS model.

Keywords

Minidisk infiltrometer Adaptive neuro-fuzzy inference system Particle swarm optimization Firefly algorithm 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Ahmadi MA (2011) Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. J Pet Explor Prod Technol 1(2–4):99–106CrossRefGoogle Scholar
  2. Ahmadi MA (2012) Neural network based unified particle swarm optimization for prediction of asphaltene precipitation. Fluid Phase Equilib 314:46–51CrossRefGoogle Scholar
  3. Ahmadi MA (2015) Developing a robust surrogate model of chemical flooding based on the artificial neural network for enhanced oil recovery implications. Math Prob Eng 2015:1–9Google Scholar
  4. Ahmadi MA, Golshadi M (2012) Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. J Petrol Sci Eng 98:40–49CrossRefGoogle Scholar
  5. Ahmadi MA, Shadizadeh SR (2012) New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel 102:716–723CrossRefGoogle Scholar
  6. Ahmadi MA, Ahmadi MR, Hosseini SM, Ebadi M (2014a) Connectionist model predicts the porosity and permeability of petroleum reservoirs by means of petro-physical logs: application of artificial intelligence. J Petrol Sci Eng 123:183–200CrossRefGoogle Scholar
  7. Ahmadi MA, Ebadi M, Yazdanpanah A (2014b) Robust intelligent tool for estimating dew point pressure in retrograded condensate gas reservoirs: application of particle swarm optimization. J Petrol Sci Eng 123:7–19CrossRefGoogle Scholar
  8. Ahmadi MA, Masumi M, Kharrat R, Mohammadi AH (2014c) Gas analysis by in situ combustion in heavy-oil recovery process: experimental and modeling studies. Chem Eng Technol 37(3):409–418CrossRefGoogle Scholar
  9. Ahmadi MA, Soleimani R, Lee M, Kashiwao T, Bahadori A (2015a) Determination of oil well production performance using artificial neural network (ANN) linked to the particle swarm optimization (PSO) tool. Petroleum 1(2):118–132CrossRefGoogle Scholar
  10. Ahmadi MH, Ahmadi MA, Sadatsakkak SA, Feidt M (2015b) Connectionist intelligent model estimates output power and torque of stirling engine. Renew Sustain Energy Rev 50:871–883CrossRefGoogle Scholar
  11. Ali Ahmadi M, Ahmadi A (2016) Applying a sophisticated approach to predict CO2 solubility in brines: application to CO2 sequestration. Int J Low-Carbon Technol 11(3):325–332CrossRefGoogle Scholar
  12. Al-Sulaiman MA, Aboukarima AM (2016) Distribution of natural radionuclides in the surface soil in some areas of agriculture and grazing located in west of Riyadh, Saudi Arabia. J Appl Life Sci Int 7(2):1.  https://doi.org/10.9734/JALSI/2016/28502 CrossRefGoogle Scholar
  13. Angelaki A, Singh Nain S, Singh V, Sihag P (2018) Estimation of models for cumulative infiltration of soil using machine learning methods. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1531274 CrossRefGoogle Scholar
  14. Arshad RR, Sayyad G, Mosaddeghi M, Gharabaghi B (2013) Predicting saturated hydraulic conductivity by artificial intelligence and regression models. ISRN Soil Sci.  https://doi.org/10.1155/2013/308159 CrossRefGoogle Scholar
  15. Azimi H, Shabanlou S, Ebtehaj I, Bonakdari H, Kardar S (2017) Combination of computational fluid dynamics, adaptive neuro-fuzzy inference system, and genetic algorithm for predicting discharge coefficient of rectangular side orifices. J Irrig Drain Eng 143(7):04017015.  https://doi.org/10.1061/(ASCE)IR.1943-4774.0001190 CrossRefGoogle Scholar
  16. Azimi H, Bonakdari H, Ebtehaj I, Michelson DG (2018) A combined adaptive neuro-fuzzy inference system–firefly algorithm model for predicting the roller length of a hydraulic jump on a rough channel bed. Neural Comput Appl 29(6):249–258.  https://doi.org/10.1007/s00521-016-2560-9 CrossRefGoogle Scholar
  17. Baghban A, Ahmadi MA, Pouladi B, Amanna B (2015) Phase equilibrium modeling of semi-clathrate hydrates of seven commonly gases in the presence of TBAB ionic liquid promoter based on a low parameter connectionist technique. J Supercrit Fluids 101:184–192CrossRefGoogle Scholar
  18. Carsel RF, Parrish RS (1988) Developing joint probability distributions of soil water retention characteristics. Water Resour Res 24(5):755–769.  https://doi.org/10.1029/WR024i005p00755 CrossRefGoogle Scholar
  19. Ch S, Sohani SK, Kumar D, Malik A, Chahar BR, Nema AK, Panigrahi BK, Dhiman RC (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288.  https://doi.org/10.1016/j.neucom.2013.09.030 CrossRefGoogle Scholar
  20. Decagon Devices, Inc (2014)Google Scholar
  21. Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, MHS’95, IEEE, Oct 4, pp 39–43.  https://doi.org/10.1109/mhs.1995.494215
  22. Ebtehaj I, Bonakdari H, Es-haghi MS (2018a) Design of a hybrid ANFIS–PSO model to estimate sediment transport in open channels. Iran J Sci Technol Trans Civ Eng.  https://doi.org/10.1007/s40996-018-0218-9 CrossRefGoogle Scholar
  23. Ebtehaj I, Bonakdari H, Zaji AH (2018b) A new hybrid decision tree method based on two artificial neural networks for predicting sediment transport in clean pipes. Alexandria Eng J 57(3):1783–1795.  https://doi.org/10.1016/j.aej.2017.05.021 CrossRefGoogle Scholar
  24. Elbisy MS (2015) Support vector machine and regression analysis to predict the field hydraulic conductivity of sandy soil. KSCE J Civ Eng 19(7):2307–2316.  https://doi.org/10.1007/s12205-015-0210-x CrossRefGoogle Scholar
  25. Gharabaghi B, Bonakdari H, Ebtehaj I (2018) Hybrid evolutionary algorithm based on PSOGA for ANFIS designing in prediction of no-deposition bed load sediment transport in sewer pipe. In: Science and information conference, pp 106-118, Springer, Cham. doi.org/ https://doi.org/10.1007/978-3-030-01177-2_8
  26. Gholami A, Bonakdari H, Ebtehaj I, Gharabaghi B, Khodashenas SR, Talesh SH, Jamali A (2018) A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS. Eng Geol 18(239):298–309.  https://doi.org/10.1016/j.enggeo.2018.03.030 CrossRefGoogle Scholar
  27. Ghorbani MA, Shamshirband S, Haghi DZ, Azani A, Bonakdari H, Ebtehaj I (2017) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 1(172):32–38.  https://doi.org/10.1016/j.still.2017.04.009 CrossRefGoogle Scholar
  28. Gui S, Zhang R, Turner JP, Xue X (2000) Probabilistic slope stability analysis with stochastic soil hydraulic conductivity. J Geotech Geoenviron Eng 126(1):1–9.  https://doi.org/10.1061/(ASCE)1090-0241(2000)126:1(1) CrossRefGoogle Scholar
  29. Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056.  https://doi.org/10.1016/j.eswa.2014.03.053 CrossRefGoogle Scholar
  30. Lin HS, McInnes KJ, Wilding LP, Hallmark CT (1999) Effects of soil morphology on hydraulic properties II. Hydraulic pedotransfer functions. Soil Sci Soc Am J 63(4):955–961.  https://doi.org/10.2136/sssaj1999.634955x CrossRefGoogle Scholar
  31. Mayr T, Jarvis NJ (1999) Pedotransfer functions to estimate soil water retention parameters for a modified Brooks-Corey type model. Geoderma 91(1–2):1–9.  https://doi.org/10.1016/S0016-7061(98)00129-3 CrossRefGoogle Scholar
  32. Mehdipour V, Stevenson DS, Memarianfard M, Sihag P (2018) Comparing different methods for statistical modeling of particulate matter in Tehran, Iran. Air Qual Atmos Health 11(10):1155–1165.  https://doi.org/10.1007/s11869-018-0615-z CrossRefGoogle Scholar
  33. Merdun H, Çınar Ö, Meral R, Apan M (2006) Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil Tillage Res 90(1–2):108–116.  https://doi.org/10.1016/j.still.2005.08.011 CrossRefGoogle Scholar
  34. Minasny B, McBratney AB (2002) The efficiency of various approaches to obtaining estimates of soil hydraulic properties. Geoderma 107(1–2):55–70.  https://doi.org/10.1016/S0016-7061(01)00138-0 CrossRefGoogle Scholar
  35. Mohanty BP, Kanwar RS, Everts CJ (1994) Comparison of saturated hydraulic conductivity measurement methods for a glacial-till soil. Soil Sci Soc Am J 58(3):672–677.  https://doi.org/10.2136/sssaj1994.03615995005800030006x CrossRefGoogle Scholar
  36. Nain SS, Sihag P, Luthra S (2018) Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy. MethodsX.  https://doi.org/10.1016/j.mex.2018.04.006 CrossRefGoogle Scholar
  37. Parsaie A, Haghiabi AH (2017) Mathematical expression of discharge capacity of compound open channels using MARS technique. J Earth Syst Sci 126(2):20.  https://doi.org/10.1007/s12040-017-0807-1 CrossRefGoogle Scholar
  38. Parsaie A, Haghiabi AH, Saneie M, Torabi H (2016) Applications of soft computing techniques for prediction of energy dissipation on stepped spillways. Neural Comput Appl.  https://doi.org/10.1007/s00521-016-2667-z CrossRefGoogle Scholar
  39. Qasem SN, Ebtehaj I, Bonakdari H (2017) Potential of radial basis function network with particle swarm optimization for prediction of sediment transport at the limit of deposition in a clean pipe. Sustain Water Resour Manag 3(4):391–401.  https://doi.org/10.1007/s40899-017-0104-9 CrossRefGoogle Scholar
  40. Reynolds WD, Elrick DE (1991) Determination of hydraulic conductivity using a tension infiltrometer. Soil Sci Soc Am J 55(3):633–639.  https://doi.org/10.2136/sssaj1991.03615995005500030001x CrossRefGoogle Scholar
  41. Reynolds WD, Zebchuk WD (1996) Hydraulic conductivity in a clay soil: two measurement techniques and spatial characterization. Soil Sci Soc Am J 60(6):1679–1685.  https://doi.org/10.2136/sssaj1996.03615995006000060011x CrossRefGoogle Scholar
  42. Shabri A, Suhartono (2012) Streamflow forecasting using least-squares support vector machines. Hydrol Sci J 57(7):1275–1293.  https://doi.org/10.1080/02626667.2012.714468 CrossRefGoogle Scholar
  43. Sihag P, Tiwari NK, Ranjan S (2017) Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2017.1381861 CrossRefGoogle Scholar
  44. Sihag P, Singh B, Gautam S, Debnath S (2018a) Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques. Applied Water Science. 8(6):187.  https://doi.org/10.1007/s13201-018-0835-2 CrossRefGoogle Scholar
  45. Sihag P, Singh B, Sepah Vand A, Mehdipour V (2018b) Modeling the infiltration process with soft computing techniques. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1464408 CrossRefGoogle Scholar
  46. Sihag P, Tiwari NK, Ranjan S (2018c) Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1439776 CrossRefGoogle Scholar
  47. Singh B, Sihag P, Singh K, Kumar S (2018) Estimation of trapping efficiency of vortex tube silt ejector. Int J River Basin Manag.  https://doi.org/10.1080/15715124.2018.1476367 just-accepted CrossRefGoogle Scholar
  48. Taskinen A, Sirviö H, Bruen M (2008) Modelling effects of spatial variability of saturated hydraulic conductivity on auto correlated overland flow data: linear mixed model approach. Stoch Environ Res Risk Assess 22(1):67–82.  https://doi.org/10.1007/s00477-006-0099-5 MathSciNetCrossRefzbMATHGoogle Scholar
  49. Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil Sci Soc Am J 44(5):892–898.  https://doi.org/10.2136/sssaj1980.03615995004400050002x CrossRefGoogle Scholar
  50. Vand AS, Sihag P, Singh B, Zand M (2018) Comparative evaluation of infiltration models. KSCE J Civ Eng 22(10):4173–4184.  https://doi.org/10.1007/s12205-018-1347-1 CrossRefGoogle Scholar
  51. Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms, Oct 26. Springer, Berlin, pp 169-178,  https://doi.org/10.1007/978-3-642-04944-6_14
  52. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspir Comput 2(2):78–84.  https://doi.org/10.1504/IJBIC.2010.032124 CrossRefGoogle Scholar
  53. Yaseen ZM, Ebtehaj I, Bonakdari H, Deo RC, Mehr AD, Mohtar WH, Diop L, El-Shafie A, Singh VP (2017) Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model. J Hydrol 1(554):263–276.  https://doi.org/10.1016/j.jhydrol.2017.09.007 CrossRefGoogle Scholar
  54. Zhang R (1997) Determination of soil sorptivity and hydraulic conductivity from the disk infiltrometer. Soil Sci Soc Am J 61(4):1024–1030.  https://doi.org/10.2136/sssaj1997.03615995006100040005x CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.National Institute of TechnologyKurukshetraIndia
  2. 2.Department of Soil Science and EngineeringUniversity of TabrizTabrizIran
  3. 3.National Institute of TechnologyHamirpurIndia
  4. 4.Department of Civil EngineeringRazi UniversityKermanshahIran
  5. 5.Environmental Research CenterRazi UniversityKermanshahIran

Personalised recommendations