Estimating Saturated Hydraulic Conductivity from Soil Physical Properties using Neural Networks Model
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).
[1] A. Hazen. Discussion of 'Dams on sand foundations' by A. C. Koenig,
Trans. Am. Soc. Civ. Eng., 1911, 73:199-203.
[2] J. Kozeny. Ueber kapillare Leitung des Wassers im Boden. Wien, Akad.
Wiss., 1927, 136:271.
[3] P. C. Carman. The determination of the specific surface of powders. J.
Soc. Chem. Ind. Trans., 1938,57:225.
[4] P. C. Carman. Flow of gases through porous media, Butterworths.
Scientific Publications, London, 1956.
[5] G. S. Campbell. Soil Physics with Basic. Elsevier, New York, 1985.
[6] H. Vereecken, J. Maes, J. Feyen. Estimating unsaturated hydraulic
conductivity from easily measured soil properties. Soil Sci., 1990,
149:1-12.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] W. D. Carrier. Goodbye, Hazen; Hello, Kozeny-Carman. Journal of
Geotech. and Geoenviron. Eng., 2003, 129:1054-1056.
[13] 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.
[14] 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.
[15] D. P. Franzmeier. Estimation of hydraulic conductivity from effective
porosity data for some Indiana soils. Soil Sci. Soc. Am. J., 1991,
55:1801-1803.
[16] I. Messing. Estimation of saturated hydraulic conductivity in clay soils
from soil moisture retention data. Soil Sci. Soc. Am. J., 1989, 53:665-
668.
[17] 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.
[18] 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.
[19] R. H. Brooks, A. T. Corey. Hydraulic properties of porous media.
Colorado State University, Hydrology, 1964, Paper No. 3, 27 p.
[20] H. Han, D. Gimenez, L. Lilly. Textural averages of saturated soil
hydraulic conductivity predicted from water retention data. Geoderma,
2008, 146:121-128.
[21] M. Menhaj. Fundamental of artificial neural network. Amirkabir Press,
2000 (in Persian).
[22] 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.
[23] 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.
[24] 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).
[25] 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.
[26] 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.
[27] M. Th. van Genuchten. A closed-form equation for predicting the
hydraulic conductivity of unsaturated soils, Soil Sci. Am. J., 1980,
44:892-898.
[28] 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.
[29] 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.
[30] E. R. Levine, D. S. Kimes, V. G. Sigillito. Classifying soil structure
using neural networks. Ecol. Model., 1996, 92:101-108.
[31] 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.
[32] 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.
[33] The MathWorks, Inc. MATLAB: The Language of Technical
Computing. version 7.5, 2008.
[34] 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.
[35] 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.
[1] A. Hazen. Discussion of 'Dams on sand foundations' by A. C. Koenig,
Trans. Am. Soc. Civ. Eng., 1911, 73:199-203.
[2] J. Kozeny. Ueber kapillare Leitung des Wassers im Boden. Wien, Akad.
Wiss., 1927, 136:271.
[3] P. C. Carman. The determination of the specific surface of powders. J.
Soc. Chem. Ind. Trans., 1938,57:225.
[4] P. C. Carman. Flow of gases through porous media, Butterworths.
Scientific Publications, London, 1956.
[5] G. S. Campbell. Soil Physics with Basic. Elsevier, New York, 1985.
[6] H. Vereecken, J. Maes, J. Feyen. Estimating unsaturated hydraulic
conductivity from easily measured soil properties. Soil Sci., 1990,
149:1-12.
[7] 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.
[8] 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.
[9] 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.
[10] 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.
[11] 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.
[12] W. D. Carrier. Goodbye, Hazen; Hello, Kozeny-Carman. Journal of
Geotech. and Geoenviron. Eng., 2003, 129:1054-1056.
[13] 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.
[14] 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.
[15] D. P. Franzmeier. Estimation of hydraulic conductivity from effective
porosity data for some Indiana soils. Soil Sci. Soc. Am. J., 1991,
55:1801-1803.
[16] I. Messing. Estimation of saturated hydraulic conductivity in clay soils
from soil moisture retention data. Soil Sci. Soc. Am. J., 1989, 53:665-
668.
[17] 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.
[18] 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.
[19] R. H. Brooks, A. T. Corey. Hydraulic properties of porous media.
Colorado State University, Hydrology, 1964, Paper No. 3, 27 p.
[20] H. Han, D. Gimenez, L. Lilly. Textural averages of saturated soil
hydraulic conductivity predicted from water retention data. Geoderma,
2008, 146:121-128.
[21] M. Menhaj. Fundamental of artificial neural network. Amirkabir Press,
2000 (in Persian).
[22] 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.
[23] 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.
[24] 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).
[25] 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.
[26] 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.
[27] M. Th. van Genuchten. A closed-form equation for predicting the
hydraulic conductivity of unsaturated soils, Soil Sci. Am. J., 1980,
44:892-898.
[28] 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.
[29] 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.
[30] E. R. Levine, D. S. Kimes, V. G. Sigillito. Classifying soil structure
using neural networks. Ecol. Model., 1996, 92:101-108.
[31] 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.
[32] 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.
[33] The MathWorks, Inc. MATLAB: The Language of Technical
Computing. version 7.5, 2008.
[34] 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.
[35] 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.
@article{"International Journal of Earth, Energy and Environmental Sciences:62236", author = "B. Ghanbarian-Alavijeh and A.M. Liaghat and S. Sohrabi", title = "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).", keywords = "Neural network, Saturated hydraulic conductivity,Soil physical properties.", volume = "4", number = "2", pages = "100-6", }