Modeling Earth Systems and Environment

, Volume 4, Issue 1, pp 189–198 | Cite as

Prediction of unsaturated hydraulic conductivity using fuzzy logic and artificial neural network

Original Article
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Abstract

Knowledge of hydraulic properties is necessary for hydrological studies, artificial recharge of the aquifer, watershed management and agriculture system. The major objective of this study was to develop a fuzzy logic and artificial neural network (ANN) based models for estimating the unsaturated hydraulic conductivity of soil (Ku). A mini disk infiltrometer, being handy used for determining infiltration characteristics. In this study mini Disk Infiltrometer (Decagon Devices, Inc.) at a suction head varying from 1 to 6 cm was used for determining Ku of sandy soil. All the measurements have been done on predetermined initial condition of different proportions of rice husk ash and fly ash mixed with sand. For modeling randomely selected 70% data was applied for training and residual 30% for the test. Comparison of results show that the prediction with ANN approach works well with correlation of coefficient value of 0.8662 (root mean square error 4.5607 cm/h).

Keywords

Unsaturated hydraulic conductivity Fuzzy logic Artificial neural network Correlation coefficient Root mean square error 

References

  1. Aggarwal P, Aggarwal Y, Siddique R, Gupta S, Garg H (2013) Fuzzy logic modeling of compressive strength of high-strength concrete (HSC) with supplementary cementitious material. J Sustain Cem Based Mater 2(2):128–143.  https://doi.org/10.1080/21650373.2013.801800 CrossRefGoogle Scholar
  2. Al-Sulaiman MA, Aboukarima AM (2015) Prediction of unsaturated hydraulic conductivity of agricultural soils using artificial neural network and c. Biosci Biotechnol Res Asia 12(3):2261–2272CrossRefGoogle Scholar
  3. Angelaki A, Sakellariou-Makrantonaki M, Tzimopoulos C (2013) Theoretical and experimental research of cumulative infiltration. Transp Porous Media 100(2):247–257.  https://doi.org/10.1007/s11242-013-0214-2 CrossRefGoogle Scholar
  4. Devices D (2014) Mini Disk Infiltrometer user’s manual version 10. Decagon Devices, Inc.. PullmanGoogle Scholar
  5. Emami H, SHorafa M, Neyshabouri MR. (2012) Evaluation of hydraulic conductivity at inflection point of soil moisture characteristic curve as a matching point for some soil unsaturated hydraulic conductivity models. JWSS Isfahan Univ Technol 16(59):169–182. http://jstnar.iut.ac.ir/article-1-2206-en.html
  6. Erzin Y, Gumaste SD, Gupta AK, Singh DN (2009) Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils. Can Geotech J 46(8):955–968.  https://doi.org/10.1139/T09-035 CrossRefGoogle Scholar
  7. Fereshte FH (2014) Evaluation of artificial neural network and regression PTFS in estimating some soil hydraulic parameters. Proenviron Promediu 7(17):10–20Google Scholar
  8. Ghanbarian-Alavijeh B, Liaghat AM, Sohrabi S (2010) Estimating saturated hydraulic conductivity from soil physical properties using neural networks model. World Acad Sci Eng Technol 4:108–113Google Scholar
  9. Gülser C, Candemir F (2008) Prediction of saturated hydraulic conductivity using some moisture constants and soil physical properties. In: Proceeding Balwois, Macedonia, pp 1–5Google Scholar
  10. Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  11. Heddam S (2016) Simultaneous modelling and forecasting of hourly dissolved oxygen concentration (DO) using radial basis function neural network (RBFNN) based approach: a case study from the Klamath River, Oregon, USA. Model Earth Syst Environ 2(3):135.  https://doi.org/10.1007/s40808-016-0197-4 CrossRefGoogle Scholar
  12. Kalkhajeh YK, Arshad RR, Amerikhah H, Sami M (2012) Multiple linear regression, artificial neural network (MLP, RBF) and anfis models for modeling the saturated hydraulic conductivity (a case study: Khuzestan province, southwest Iran). Int J Agric 2(3):255–265Google Scholar
  13. Lakzian A, Aval MB, Gorbanzadeh N (2010) Comparison of pattern recognition, artificial neural network and pedotransfer functions for estimation of soil water parameters. Notulae Sci Biol 2(3):114–120Google Scholar
  14. Moosavi AA, Sepaskhah A (2012) Artificial neural networks for predicting unsaturated soil hydraulic characteristics at different applied tensions. Arch Agron Soil Sci 58(2):125–153.  https://doi.org/10.1080/03650340.2010.512289 CrossRefGoogle Scholar
  15. Nosrati KF, Movahedi NS, Hezarjaribi A, Roshani GA, Dehghani AA (2012) Using artificial neural networks to estimate saturated hydraulic conductivity from easily available soil properties. Electron J Soil Manag Sustain Prod 2(1):95–110Google Scholar
  16. Parsaie A (2016a) Predictive modeling the side weir discharge coefficient using neural network. Model Earth Syst Environ 2(2):63.  https://doi.org/10.1007/s40808-016-0123-9 CrossRefGoogle Scholar
  17. Parsaie A (2016b) Analyzing the distribution of momentum and energy coefficients in compound open channel. Model Earth Syst Environ 2(1):15.  https://doi.org/10.1007/s40808-015-0054-x CrossRefGoogle Scholar
  18. Parsaie A, Haghiabi A (2015a) The effect of predicting discharge coefficient by neural network on increasing the numerical modeling accuracy of flow over side weir. Water Resour Manag 29(4):973–985.  https://doi.org/10.1007/s11269-014-0827-4 CrossRefGoogle Scholar
  19. Parsaie A, Haghiabi AH (2015b) Predicting the longitudinal dispersion coefficient by radial basis function neural network. Model Earth Syst Environ 1(4):34–42.  https://doi.org/10.1007/s40808-015-0037-y CrossRefGoogle Scholar
  20. Parsaie A, Yonesi HA, Najafian S (2015) Predictive modeling of discharge in compound open channel by support vector machine technique. Model Earth Syst Environ 1(1–2):1.  https://doi.org/10.1007/s40808-015-0002-9 CrossRefGoogle Scholar
  21. Parsaie A, Najafian S, Shamsi Z (2016) Predictive modeling of discharge of flow in compound open channel using radial basis neural network. Model Earth Syst Environ 2(3):150.  https://doi.org/10.1007/s40808-016-0207-6 CrossRefGoogle Scholar
  22. Sarmadian F, Mehrjardi RT. (2010) Development of pedotransfer functions to predict soil hydraulic properties in Golestan Province, Iran. In: 19th World congress of soil science, soil solutions for a changing world, pp 1–6Google Scholar
  23. Schaap MG, Leij FJ (1998) Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil Tillage Res 47(1):37–42.  https://doi.org/10.1016/S0167-1987(98)00070-1 CrossRefGoogle Scholar
  24. Schuh WM, Bauder JW (1986) Effect of soil properties on hydraulic conductivity–moisture relationships. Soil Sci Soc Am J 50(4):848–855.  https://doi.org/10.2136/sssaj1986.03615995005000040004x CrossRefGoogle Scholar
  25. Sihag P, Tiwari NK, Ranjan S (2017a) Estimation and inter-comparison of infiltration models. Water Sci 31(1):34–43.  https://doi.org/10.1016/j.wsj.2017.03.001 CrossRefGoogle Scholar
  26. Sihag P, Tiwari NK, Ranjan S (2017b) Modelling of infiltration of sandy soil using Gaussian process regression. Model Earth Syst Enviro 3(3):1091–1100.  https://doi.org/10.1007/s40808-017-0357-1 CrossRefGoogle Scholar
  27. Sihag P, Tiwari NK, Ranjan S (2017c) Prediction of unsaturated hydraulic conductivity using adaptive neuro-fuzzy inference system (ANFIS). ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2017.1381861 Google Scholar
  28. Sihag P, Jain P, Kumar M (2018a). Modelling of impact of water quality on recharging rate of storm water filter system using various kernel function based regression. Model Earth Syst Environ.  https://doi.org/10.1007/s40808-017-0410-0 Google Scholar
  29. Sihag P, Tiwari NK, Ranjan S (2018b) Support vector regression-based modeling of cumulative infiltration of sandy soil. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1439776 Google Scholar
  30. Siltecho S, Hammecker C, Sriboonlue V, Clermont-Dauphin C, Trelo-Ges V, Antonino ACD, Angulo-Jaramillo R (2014) Use of field and laboratory methods for estimating unsaturated hydraulic properties under different land-use. Hydrol Earth Syst Sci 11(6):6099–6137.  https://doi.org/10.5194/hess-19-1193-2015 CrossRefGoogle Scholar
  31. Singh B, Sihag P, Singh K (2017) Modelling of impact of water quality on infiltration rate of soil by random forest regression. Model Earth Syst Environ 3(3):999–1004.  https://doi.org/10.1007/s40808-017-0347-3 CrossRefGoogle Scholar
  32. Skaggs TH, Arya LM, Shouse PJ, Mohanty BP (2001) Estimating particle-size distribution from limited soil texture data. Soil Sci Soc Am J 65(4):1038–1044.  https://doi.org/10.2136/sssaj2001.6541038x CrossRefGoogle Scholar
  33. Tamari S, Wösten JH, Ruiz-Suarez JC (1996) Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci Soc Am J 60(6):1732–1741.  https://doi.org/10.2136/sssaj1996.03615995006000060018x CrossRefGoogle Scholar
  34. Tiwari NK, Sihag P, Ranjan S (2017) Modeling of infiltration of soil using adaptive neuro-fuzzy inference system (ANFIS). J Eng Technol Educ 11(1):13–21Google Scholar
  35. Tiwari NK, Sihag P, Kumar S, Ranjan S (2018) Prediction of trapping efficiency of vortex tube ejector. ISH J Hydraul Eng.  https://doi.org/10.1080/09715010.2018.1441752 Google Scholar
  36. Van Genuchten MT (1980) A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil Sci Soc Am J 44(5):892–898CrossRefGoogle Scholar
  37. Zhang R (1997) Determination of soil sorptivity and hydraulic conductivity from the disk infiltrometer. Soil Sci Soc Am J 61(4):1024–1030CrossRefGoogle Scholar
  38. Zadeh LA (1965) Fuzzy sets. Inform Contr 8(3):338–353CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Institute of TechnologyKurukshetraIndia

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