Commenced in January 2007
Paper Count: 30172
Estimating Saturated Hydraulic Conductivity from Soil Physical Properties using Neural Networks Model
Abstract:Saturated hydraulic conductivity is one of the soil hydraulic properties which is widely used in environmental studies especially subsurface ground water. Since, its direct measurement is time consuming and therefore costly, indirect methods such as pedotransfer functions have been developed based on multiple linear regression equations and neural networks model in order to estimate saturated hydraulic conductivity from readily available soil properties e.g. sand, silt, and clay contents, bulk density, and organic matter. The objective of this study was to develop neural networks (NNs) model to estimate saturated hydraulic conductivity from available parameters such as sand and clay contents, bulk density, van Genuchten retention model parameters (i.e. r θ , α , and n) as well as effective porosity. We used two methods to calculate effective porosity: : (1) eff s FC φ =θ -θ , and (2) inf φ =θ -θ eff s , in which s θ is saturated water content, FC θ is water content retained at -33 kPa matric potential, and inf θ is water content at the inflection point. Total of 311 soil samples from the UNSODA database was divided into three groups as 187 for the training, 62 for the validation (to avoid over training), and 62 for the test of NNs model. A commercial neural network toolbox of MATLAB software with a multi-layer perceptron model and back propagation algorithm were used for the training procedure. The statistical parameters such as correlation coefficient (R2), and mean square error (MSE) were also used to evaluate the developed NNs model. The best number of neurons in the middle layer of NNs model for methods (1) and (2) were calculated 44 and 6, respectively. The R2 and MSE values of the test phase were determined for method (1), 0.94 and 0.0016, and for method (2), 0.98 and 0.00065, respectively, which shows that method (2) estimates saturated hydraulic conductivity better than method (1).
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080700Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2125
 A. Hazen. Discussion of 'Dams on sand foundations' by A. C. Koenig, Trans. Am. Soc. Civ. Eng., 1911, 73:199-203.
 J. Kozeny. Ueber kapillare Leitung des Wassers im Boden. Wien, Akad. Wiss., 1927, 136:271.
 P. C. Carman. The determination of the specific surface of powders. J. Soc. Chem. Ind. Trans., 1938,57:225.
 P. C. Carman. Flow of gases through porous media, Butterworths. Scientific Publications, London, 1956.
 G. S. Campbell. Soil Physics with Basic. Elsevier, New York, 1985.
 H. Vereecken, J. Maes, J. Feyen. Estimating unsaturated hydraulic conductivity from easily measured soil properties. Soil Sci., 1990, 149:1-12.
 J. H. M. Wösten. Pedotransfer functions to evaluate soil quality. In: Gregorich, E.G., Carter, M.R.ŽEds.., Soil Quality for Crop Production and Ecosystem Health. Developments in Soils Science, vol. 25, Elsevier, Amsterdam, 1997, pp. 221-245.
 J. H. M. Wösten, A. Lilly, A. Nemes, C. Le Bas. Development and use of a database of hydraulic properties of European soils. Geoderma, 1999, 90:169-185.
 B. J. Cosby, G. M. Hornberger, R. B. Clapp, T. R. Ginn. A statistical exploration of soil moisture characteristics to the physical properties of soils. Water Res. Res., 1984,20:682-690.
 D. L. Brakensiek, W. J. Rawls, G. R. Stephenson. Modifying SCS hydrologic soil groups and curve numbers for rangeland soils. ASAE, 1984, Paper No. PNR-84-203, St. Joseph, MI.
 M. G. Schaap, F. J. Leij, M. Th. van Genuchten. Rosetta: A computer program for estimating soil hydraulic parameters with hierarchical pedotransfer functions, J. Hydrol., 2001, 251:163-176.
 W. D. Carrier. Goodbye, Hazen; Hello, Kozeny-Carman. Journal of Geotech. and Geoenviron. Eng., 2003, 129:1054-1056.
 L. R. Ahuja, J. W. Naney, R. E. Green, D. R. Nielsen. Macroporosity to characterize spatial variability of hydraulic conductivity and effects of land management. Soil Sci. Soc. Am. J., 1984, 48:699-702.
 L. R. Ahuja, D. K. Cassel, R. R. Bruce, B. B. Barnes. Evaluation of spatial distribution of hydraulic conductivity using effective porosity data. Soil Sci., 1989, 148:404-411.
 D. P. Franzmeier. Estimation of hydraulic conductivity from effective porosity data for some Indiana soils. Soil Sci. Soc. Am. J., 1991, 55:1801-1803.
 I. Messing. Estimation of saturated hydraulic conductivity in clay soils from soil moisture retention data. Soil Sci. Soc. Am. J., 1989, 53:665- 668.
 D. J. Timlin, L. R. Ahuja, R. D. Williams. Methods to estimate soil hydraulic parameters for regional scale applications of mechanistic methods. In Application of GIS to the Modeling of Non-point Source Pollutants in the Vadose Zone. SSSA Agronomy, Madison, WI, 1996, pp. 185-203.
 Y. A. Pachepsky, D. J. Timlin, L. R. Ahuja. Estimating saturated soil hydraulic conductivity using water retention data and neural networks. Soil Sci., 1999, 164:552-560.
 R. H. Brooks, A. T. Corey. Hydraulic properties of porous media. Colorado State University, Hydrology, 1964, Paper No. 3, 27 p.
 H. Han, D. Gimenez, L. Lilly. Textural averages of saturated soil hydraulic conductivity predicted from water retention data. Geoderma, 2008, 146:121-128.
 M. Menhaj. Fundamental of artificial neural network. Amirkabir Press, 2000 (in Persian).
 A. Jain, A. Kumar. An evaluation of artificial neural network technique for the determination of infiltration model parameters. Applied Soft Computing, 2006, 6:272-282.
 M. Amini, K. C. Abbaspour, H. Khademi, N. Fathianpour, M. Afyuni, R. Schulin. Neural network models to predict cation exchange capacity in arid regions of Iran. Euro. J. Soil Sci., 2005, 56:551-559.
 M. Doaee, M. Shabanpour sharestani, F. Bagheri. Modeling saturated hydraulic conductivity in clay soils in guilan province (Iran) using artificial neural networks. J. Agric. Sci., 2005, 1:41-48 (in Persian).
 K. Parasuraman, A. Elshorbagy, B.C. Si. Estimating saturated hydraulic conductivity in spatially variable fields using neural network ensembles. Soil Sci. Soc. Am. J., 2006, 70:1851-1859.
 F. J. Leij, W. J. Alves, M. Th. van Genuchten, J. R. Williams. Unsaturated soil hydraulic database, UNSODA 1.0 user-s manual. Rep. EPA/600/R96/095. USEPA, Ada, OK, 1996.
 M. Th. van Genuchten. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils, Soil Sci. Am. J., 1980, 44:892-898.
 M.Th. van Genuchten, F.J. Leij, S.R. Yates. The RETC code for quantifying the hydraulic functions of unsaturated soils. USDA, US Salinity Laboratory, Riverside, CA. United States Environmental Protection Agency, document EPA/600/2-91/065, 1991.
 A. R. Dexter. Soil physical quality Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma, 2004, 120:201-214.
 E. R. Levine, D. S. Kimes, V. G. Sigillito. Classifying soil structure using neural networks. Ecol. Model., 1996, 92:101-108.
 J. Morshed, J. J. Kaluarachchi. Application of artificial neural network and genetic algorithm in flow and transport simulations. Adv. Water Res., 1998, 22:145-158.
 H. Yoon, Y. Hyun, K-K. Lee. Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networks. J. Hydrol., 2007, 335:68-77.
 The MathWorks, Inc. MATLAB: The Language of Technical Computing. version 7.5, 2008.
 M. Schaap, F.J. Leij, M.Th. van Genuchten. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci. Soc. Am. J., 1998, 62:847-855.
 A. Nemes, W. J. Rawls, Y. A. Pachepsky. Influence of organic matter on the estimation of saturated hydraulic conductivity. Soil Sci. Soc. Am. J., 2005, 69:1330-1337.