Developing Pedotransfer Functions for Estimating Some Soil Properties using Artificial Neural Network and Multivariate Regression Approaches
Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
[1] M. Amini, K.C. Abbaspour, H. Khademi, N. Fathianpour, M. Afyuni
and R. Schulin, "Neural network models to predict cation exchange
capacity in arid regions of Iran", Eur. J. Soil Sci., Vol. 53, pp 748-757,
2005.
[2] L. Baker and D. Ellison, "Optimisation of pedotransfer functions using
an artificial neural network ensemble method", Geoderma, Vol.144, pp
212-224, 2008.
[3] M. Banimahd, S.S. Yasrobi and P.K. Woodward, "Artificial neural
network for stress-strain behavior of sandy soils: Knowledge based
verification", Comput. Geotech., Vol. 32, pp 377-386, 2005.
[4] G. R. Blake and K. H. Hartge, "Particle density", In: A. Klute, (ed)
Methods of soil analysis, Part 1, Agron Monogr 9, ASA, Madison, WI,
pp 377-382, 1986.
[5] J. Bouma, "Using soil survey data for quantitative land evaluation",
Advances in Soil Science., Vol. 9, pp 177-213, 1989.
[6] D. K. Cassel and D. R. Nielsen, "Field capacity and available water
capacity". In: A. Klute, (Ed) Methods of Soil Analysis, Part 1, second
edn. Agron Monogr 9, ASA and SSSA, Madison, WI, pp 901-926,
1986.
[7] L. Cavazza, A. Patruno and E. Cirillo, "Field capacity in soils with a
yearly oscillating water table", Biosystems Engineering., Vol. 98, pp
364-370, 2007.
[8] J. A. Field, J. C. Parker and N. L. Powell, "Comparison of field- and
laboratory measured and predicted hydraulic properties of a soil with
macropores", Soil Sci., Vol. pp 138, 385-396, 1984.
[9] J. Givi, S. O. Prasherb and R. M. Patel, "Evaluation of pedotransfer
functions in predicting the soil water contents at field capacity and
wilting point", Agricultural Water Management., Vol. 70, pp 83-96,
2004.
[10] S. A. Heusher, C. C. Brandt and P. M. Jardin, "Using soil physical and
chemical properties to estimate bulk density", Soil Sci Soc Am J., Vol.
69, pp 51-56, 2005.
[11] D. Hillel, " Environmental Soil Physics" , Academic Press, New York,
USA, 1998.
[12] A. Jain and A. Kumar, "An evaluation of artificial neural network
technique for the determination of infiltration model parameters", Appl.
Soft Comput., Vol. 6, pp 272-282, 2006.
[13] F. Karaca and B. Ozkaya, "NN-LEAP: A neural network-based model
for controlling leachate flow-rate in a municipal solid waste landfill
site", Environ. Modell. Software., Vol. 21, pp 1190-1197, 2006.
[14] R. Kaur, S. Kumar and H.P. Gurung, "A pedotransfer function soil data
and its comparison with existing PTFs", Aust. J. Soil Res., Vol. 40, pp
847- 857, 2002.
[15] A. Keller, B. Von Steiger, S.T. Vander Zee and R. Schulin, "A
stochastic empirical model for regional heavy metal balances in
agroecosystems". Journal of Environmental Quality., Vol. 30, pp 1976-
1989, 2001.
[16] E.J.W. Koekkoek and H. Booltink, "Neural network models to predict
soil water retention", Eur. J. Soil Sci., Vol. 50, pp 489-495, 1999.
[17] H.R. Lake, A. Akbarzadeh and R. Taghizadeh Mehrjardi, "Development
of pedotransfer functions (PTFs) to predict soil physico-chemical and
hydrological characteristics in southern coastal zones of the Caspian
Sea", Journal of Ecology and the Natural Environment., Vol. 1, No.7,
pp 160-172, 2009.
[18] L.A. Manrique, C.A. Jones and P.T. Dyke, "Predicting cation exchange
capacity from soil physical and chemical properties", Soil Science
Society of America Journal., Vol. 50, pp 787-794, 1991.
[19] C. Manyame, C.L. Morgan, J.L. Heilman, D. Fatondji, B. Gerard and
W.A. Payne, "Modeling hydraulic properties of sandy soils of Niger
using pedotransfer functions", Geoderma., Vol. 141, pp 407-415, 2007.
[20] H. Merdun, O. C─▒nar, R. Meral and M. Apan, "Comparison of artificial
neural network and regression pedotransfer functions for prediction of
soil water retention and saturated hydraulic conductivity", Soil Till.Res.,
Vol. 90, pp 108-116, 2006.
[21] A. Mermoud and D. Xu, "Comparative analysis of three methods to
generate soil hydraulic functions", Soil Till. Res., Vol. 87, pp 89-100,
2006.
[22] B. Minasny and A.B. McBratney, "The neuro-m methods for fitting
neural network parametric pedotransfer functions", Soil Sci. Soc. Am.
J., Vol. 66, pp 352-361, 2002.
[23] B. Minasny, A.B. McBratney and K.L. Bristow, "Comparison of
different approaches to the development of pedotransfer functions for
water retention curves", Geoderma., Vol. 93, pp 225- 253, 1999.
[24] M. Najafi and J. Givi, "Evaluation of prediction of bulk density by
artificial neural network and PTFs", 10th Iranian Soil Science Congress,
Karaj., pp 680-681, 2006.
[25] D.W. Nelson and L.E. Sommers, "Total carbon, organic carbon, and
organic matter". In: A.L. Page, R.H. Miller and D.R. Keeney (Eds.),
Methods of Soil Analysis. Part II, 2nd ed. American Society of
Agronomy, Madison, WI, USA, pp: 539-580, 1982.
[26] M. H. Omid, M. Omid and M. E. Varaki, "Modeling hydraulic jumps
with artificial neural networks", Proceedings of ICE-Water
Management., Vol. 158, No. 2, pp 65-70, 2005.
[27] M. Omid, A. Baharlooei and H. Ahmadi, "Modeling drying kinetics of
pistachio nuts with multilayer feed-forward neural network", Drying
Tech., Vol. 27, pp 1069-1077. 2009.
[28] Y.A. Pachepsky, D. Timlin and G. Varallyay, "Artificial neural
networks to estimate soil water retention from easily measurable data",
Soil Sci. Soc. Am. J., Vol. 60, pp 727-733, 1996.
[29] Y. A. Pachepsky and W. J. Rawls, "Soil structure and pedotransfer
functions", Eur J Soil Sci., Vol. 54, pp 443- 451, 2003.
[30] B. J. Park, W. Pedrycz and S. K. Oh, "Polynomial-based radial basis
function neural networks (P-RBFNNs) and their application to pattern
classification", Applied Intelligence., Vol. 32, pp 27-46, 2010.
[31] F. Sarmadian, R. Taghizadeh Mehrjardi and A. Akbarzadeh, "Modeling
of some soil properties using artificial neural network and multivariate
regression in Gorgan province, north of Iran", Australian J. of Basic and
Applied Sci., Vol. 3, No. 1, pp 323-329, 2009.
[32] M.G. Schaap and F.J. Leij, "Using neural networks to predict soil water
retention and soil hydraulic conductivity", Soil Till. Res., Vol. 47, pp
37-42, 1998.
[33] M.G. Schaap, F.J. Leij and M.Th. Van Genuchten, "Neural network
analysis for hierarchical prediction of soil hydraulic properties", Soil
Sci. Soc. Am. J., Vol. 62, pp 847-855, 1998.
[34] W. M. Shuh, R. D. Cline and M. D. Sweeney, "Comparison of a
laboratory procedure and a textural model for predicting in situ water
retention", Soil Sci Soc Am J., Vol. 52, pp 1218-1227, 1988.
[35] D.L. Sparks, A.L. Page, P.A. Helmke, R.H. Leoppert, P.N. Soltanpour,
M.A. Tabatabai, G.T. Johnston and M.E. Summer, "Methods of soil
analysis", Soil Sci. Soc. of Am. Madison, Wisconsin, 1996.
[36] Ir. C. Sys, E.Van Ranst and Ir. J. Debaveye, "Land evaluation". Part I.
Principal Land evaluation and Crop production calculation general
administration for development, Cooperation agric Pub., Vol. 1, No. 7,
pp 247, 1991.
[37] S. Tamari, J.H.M. Wosten and J.C. Ruiz-Suarez, "Testing an artificial
neural network for predicting soil hydraulic conductivity", Soil Sci. Soc.
Am. J., Vol. 60, pp 1732-1741, 1996.
[38] USDA, "Soil Survey Staff, Keys to Soil Taxonomy", 11th edition, 2010.
[39] B.D. Vos, M.V. Meirvenne, P. Quataert, J. Deckers and B. Muys,
"Predictive quality of pedotransfer functions for estimating bulk density
of forest soils", Soil Sci. Soc. Am. J., Vol. 69, pp 500-510, 2005.
[40] B. Wagner, V.R. Tarnawski, V. Hennings, U. Muller, G. Wessolek and
R. Plagge, "Evaluation of pedo-transfer functions for unsaturated soil
hydraulic conductivity using an independent data set", Geoderma.,
Vol.102, pp 275-279, 2001.
[41] J.H.M. Wösten, A. Lilly, A. Nemes and C. Le Bas, "Development and
use of a database of hydraulic properties of European soils", Geoderma.,
Vol. 90, pp 169-185, 1999.
[1] M. Amini, K.C. Abbaspour, H. Khademi, N. Fathianpour, M. Afyuni
and R. Schulin, "Neural network models to predict cation exchange
capacity in arid regions of Iran", Eur. J. Soil Sci., Vol. 53, pp 748-757,
2005.
[2] L. Baker and D. Ellison, "Optimisation of pedotransfer functions using
an artificial neural network ensemble method", Geoderma, Vol.144, pp
212-224, 2008.
[3] M. Banimahd, S.S. Yasrobi and P.K. Woodward, "Artificial neural
network for stress-strain behavior of sandy soils: Knowledge based
verification", Comput. Geotech., Vol. 32, pp 377-386, 2005.
[4] G. R. Blake and K. H. Hartge, "Particle density", In: A. Klute, (ed)
Methods of soil analysis, Part 1, Agron Monogr 9, ASA, Madison, WI,
pp 377-382, 1986.
[5] J. Bouma, "Using soil survey data for quantitative land evaluation",
Advances in Soil Science., Vol. 9, pp 177-213, 1989.
[6] D. K. Cassel and D. R. Nielsen, "Field capacity and available water
capacity". In: A. Klute, (Ed) Methods of Soil Analysis, Part 1, second
edn. Agron Monogr 9, ASA and SSSA, Madison, WI, pp 901-926,
1986.
[7] L. Cavazza, A. Patruno and E. Cirillo, "Field capacity in soils with a
yearly oscillating water table", Biosystems Engineering., Vol. 98, pp
364-370, 2007.
[8] J. A. Field, J. C. Parker and N. L. Powell, "Comparison of field- and
laboratory measured and predicted hydraulic properties of a soil with
macropores", Soil Sci., Vol. pp 138, 385-396, 1984.
[9] J. Givi, S. O. Prasherb and R. M. Patel, "Evaluation of pedotransfer
functions in predicting the soil water contents at field capacity and
wilting point", Agricultural Water Management., Vol. 70, pp 83-96,
2004.
[10] S. A. Heusher, C. C. Brandt and P. M. Jardin, "Using soil physical and
chemical properties to estimate bulk density", Soil Sci Soc Am J., Vol.
69, pp 51-56, 2005.
[11] D. Hillel, " Environmental Soil Physics" , Academic Press, New York,
USA, 1998.
[12] A. Jain and A. Kumar, "An evaluation of artificial neural network
technique for the determination of infiltration model parameters", Appl.
Soft Comput., Vol. 6, pp 272-282, 2006.
[13] F. Karaca and B. Ozkaya, "NN-LEAP: A neural network-based model
for controlling leachate flow-rate in a municipal solid waste landfill
site", Environ. Modell. Software., Vol. 21, pp 1190-1197, 2006.
[14] R. Kaur, S. Kumar and H.P. Gurung, "A pedotransfer function soil data
and its comparison with existing PTFs", Aust. J. Soil Res., Vol. 40, pp
847- 857, 2002.
[15] A. Keller, B. Von Steiger, S.T. Vander Zee and R. Schulin, "A
stochastic empirical model for regional heavy metal balances in
agroecosystems". Journal of Environmental Quality., Vol. 30, pp 1976-
1989, 2001.
[16] E.J.W. Koekkoek and H. Booltink, "Neural network models to predict
soil water retention", Eur. J. Soil Sci., Vol. 50, pp 489-495, 1999.
[17] H.R. Lake, A. Akbarzadeh and R. Taghizadeh Mehrjardi, "Development
of pedotransfer functions (PTFs) to predict soil physico-chemical and
hydrological characteristics in southern coastal zones of the Caspian
Sea", Journal of Ecology and the Natural Environment., Vol. 1, No.7,
pp 160-172, 2009.
[18] L.A. Manrique, C.A. Jones and P.T. Dyke, "Predicting cation exchange
capacity from soil physical and chemical properties", Soil Science
Society of America Journal., Vol. 50, pp 787-794, 1991.
[19] C. Manyame, C.L. Morgan, J.L. Heilman, D. Fatondji, B. Gerard and
W.A. Payne, "Modeling hydraulic properties of sandy soils of Niger
using pedotransfer functions", Geoderma., Vol. 141, pp 407-415, 2007.
[20] H. Merdun, O. C─▒nar, R. Meral and M. Apan, "Comparison of artificial
neural network and regression pedotransfer functions for prediction of
soil water retention and saturated hydraulic conductivity", Soil Till.Res.,
Vol. 90, pp 108-116, 2006.
[21] A. Mermoud and D. Xu, "Comparative analysis of three methods to
generate soil hydraulic functions", Soil Till. Res., Vol. 87, pp 89-100,
2006.
[22] B. Minasny and A.B. McBratney, "The neuro-m methods for fitting
neural network parametric pedotransfer functions", Soil Sci. Soc. Am.
J., Vol. 66, pp 352-361, 2002.
[23] B. Minasny, A.B. McBratney and K.L. Bristow, "Comparison of
different approaches to the development of pedotransfer functions for
water retention curves", Geoderma., Vol. 93, pp 225- 253, 1999.
[24] M. Najafi and J. Givi, "Evaluation of prediction of bulk density by
artificial neural network and PTFs", 10th Iranian Soil Science Congress,
Karaj., pp 680-681, 2006.
[25] D.W. Nelson and L.E. Sommers, "Total carbon, organic carbon, and
organic matter". In: A.L. Page, R.H. Miller and D.R. Keeney (Eds.),
Methods of Soil Analysis. Part II, 2nd ed. American Society of
Agronomy, Madison, WI, USA, pp: 539-580, 1982.
[26] M. H. Omid, M. Omid and M. E. Varaki, "Modeling hydraulic jumps
with artificial neural networks", Proceedings of ICE-Water
Management., Vol. 158, No. 2, pp 65-70, 2005.
[27] M. Omid, A. Baharlooei and H. Ahmadi, "Modeling drying kinetics of
pistachio nuts with multilayer feed-forward neural network", Drying
Tech., Vol. 27, pp 1069-1077. 2009.
[28] Y.A. Pachepsky, D. Timlin and G. Varallyay, "Artificial neural
networks to estimate soil water retention from easily measurable data",
Soil Sci. Soc. Am. J., Vol. 60, pp 727-733, 1996.
[29] Y. A. Pachepsky and W. J. Rawls, "Soil structure and pedotransfer
functions", Eur J Soil Sci., Vol. 54, pp 443- 451, 2003.
[30] B. J. Park, W. Pedrycz and S. K. Oh, "Polynomial-based radial basis
function neural networks (P-RBFNNs) and their application to pattern
classification", Applied Intelligence., Vol. 32, pp 27-46, 2010.
[31] F. Sarmadian, R. Taghizadeh Mehrjardi and A. Akbarzadeh, "Modeling
of some soil properties using artificial neural network and multivariate
regression in Gorgan province, north of Iran", Australian J. of Basic and
Applied Sci., Vol. 3, No. 1, pp 323-329, 2009.
[32] M.G. Schaap and F.J. Leij, "Using neural networks to predict soil water
retention and soil hydraulic conductivity", Soil Till. Res., Vol. 47, pp
37-42, 1998.
[33] M.G. Schaap, F.J. Leij and M.Th. Van Genuchten, "Neural network
analysis for hierarchical prediction of soil hydraulic properties", Soil
Sci. Soc. Am. J., Vol. 62, pp 847-855, 1998.
[34] W. M. Shuh, R. D. Cline and M. D. Sweeney, "Comparison of a
laboratory procedure and a textural model for predicting in situ water
retention", Soil Sci Soc Am J., Vol. 52, pp 1218-1227, 1988.
[35] D.L. Sparks, A.L. Page, P.A. Helmke, R.H. Leoppert, P.N. Soltanpour,
M.A. Tabatabai, G.T. Johnston and M.E. Summer, "Methods of soil
analysis", Soil Sci. Soc. of Am. Madison, Wisconsin, 1996.
[36] Ir. C. Sys, E.Van Ranst and Ir. J. Debaveye, "Land evaluation". Part I.
Principal Land evaluation and Crop production calculation general
administration for development, Cooperation agric Pub., Vol. 1, No. 7,
pp 247, 1991.
[37] S. Tamari, J.H.M. Wosten and J.C. Ruiz-Suarez, "Testing an artificial
neural network for predicting soil hydraulic conductivity", Soil Sci. Soc.
Am. J., Vol. 60, pp 1732-1741, 1996.
[38] USDA, "Soil Survey Staff, Keys to Soil Taxonomy", 11th edition, 2010.
[39] B.D. Vos, M.V. Meirvenne, P. Quataert, J. Deckers and B. Muys,
"Predictive quality of pedotransfer functions for estimating bulk density
of forest soils", Soil Sci. Soc. Am. J., Vol. 69, pp 500-510, 2005.
[40] B. Wagner, V.R. Tarnawski, V. Hennings, U. Muller, G. Wessolek and
R. Plagge, "Evaluation of pedo-transfer functions for unsaturated soil
hydraulic conductivity using an independent data set", Geoderma.,
Vol.102, pp 275-279, 2001.
[41] J.H.M. Wösten, A. Lilly, A. Nemes and C. Le Bas, "Development and
use of a database of hydraulic properties of European soils", Geoderma.,
Vol. 90, pp 169-185, 1999.
@article{"International Journal of Earth, Energy and Environmental Sciences:54314", author = "Fereydoon Sarmadian and Ali Keshavarzi", title = "Developing Pedotransfer Functions for Estimating Some Soil Properties using Artificial Neural Network and Multivariate Regression Approaches", abstract = "Study of soil properties like field capacity (F.C.) and permanent wilting point (P.W.P.) play important roles in study of soil moisture retention curve. Although these parameters can be measured directly, their measurement is difficult and expensive. Pedotransfer functions (PTFs) provide an alternative by estimating soil parameters from more readily available soil data. In this investigation, 70 soil samples were collected from different horizons of 15 soil profiles located in the Ziaran region, Qazvin province, Iran. The data set was divided into two subsets for calibration (80%) and testing (20%) of the models and their normality were tested by Kolmogorov-Smirnov method. Both multivariate regression and artificial neural network (ANN) techniques were employed to develop the appropriate PTFs for predicting soil parameters using easily measurable characteristics of clay, silt, O.C, S.P, B.D and CaCO3. The performance of the multivariate regression and ANN models was evaluated using an independent test data set. In order to evaluate the models, root mean square error (RMSE) and R2 were used. The comparison of RSME for two mentioned models showed that the ANN model gives better estimates of F.C and P.W.P than the multivariate regression model. The value of RMSE and R2 derived by ANN model for F.C and P.W.P were (2.35, 0.77) and (2.83, 0.72), respectively. The corresponding values for multivariate regression model were (4.46, 0.68) and (5.21, 0.64), respectively. Results showed that ANN with five neurons in hidden layer had better performance in predicting soil properties than multivariate regression.
", keywords = "Artificial neural network, Field capacity, Permanentwilting point, Pedotransfer functions.", volume = "4", number = "12", pages = "641-7", }
