Eurasian Journal of Soil Science
Volume 3, Issue 1, Jun 2014, Pages 1 - 6DOI: 10.18393/ejss.03148
Stable URL: http://ejss.fess.org/10.18393/ejss.03148
Copyright © 2014 The authors and Federation of Eurasian Soil Science Societies
Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province
Article first published online: 05 Jan 2014 | How to cite | Additional Information (Show All)
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Taghizadeh-Mehrjardi , R., Sarmadian , F., 2014. Estimation of infiltration rate and deep percolation water using feed-forward neural networks in Gorgan Province. Eurasian J. Soil Sci. 3(1): 1 - 6. DOI : 10.18393/ejss.03148Author information
Ruhollah Taghizadeh-Mehrjardi , Ardakan University, Faculty of Agriculture and Natural Resources, IranFereydoon Sarmadian , Tehran University, College of Agriculture and Natural Resources, Iran
Publication information
Issue published online: 30 Jun 2014Article first published online : 05 Jan 2014
Manuscript Accepted : 29 Dec 2013
Manuscript Received: 01 Aug 2013
DOI: 10.18393/ejss.03148
Stable URL: http://ejss.fesss.org/10.18393/ejss.03148
Abstract
The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, which relates input and output data and in general is difficult because these models are not known. So at present research, we compare performance of feed-forward back-propagation network to predict soil properties. Soil samples were collected from different horizons profiles located in the Gorgan Province, North of Iran. Measured soil variables included texture, organic carbon, water saturation percentage Bulk density, Infiltration rate and deep percolation. Then, multiple linear regression and neural network model were employed to develop a pedotransfer function for predicting soil parameters using easily measurable characteristics of clay, silt, SP, Bd and organic carbon. The performance of the multiple linear regression and neural network model was evaluated using a test data set by R2, RMSE and RSE. Results showed that artificial neural network with two and five neurons in hidden layer had better performance in predicting soil hydraulic properties than multivariate regression. In conclusion, the result of this study showed that both ANN and regression predicted soil properties with relatively high accuracy that showed that strong relationship between input and output data and also high accuracy in determining of data.Keywords
Infiltration rate, deep percolation, pedotransfer function
Corresponding author
References
Amini, M., Abbaspour, K. C. Khademi., H. Fathianpour., N. Afyuni., M. Schulin. R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European Journal of Soil Science 56, 551 – 559.
Besaw, L.E. Rizzo D.M. Mouser, P.J., 2006. Application of an Artificial Neural Network for Analysis of Subsurface Contamination at the Schuyler Falls Landfill, 3rd Biennial meeting of the International Environmental Modelling and Software Society. July 9-13, 2006 The Wyndham Hotel, Burlington, Vermont, USA
Blake, G.R., Hartge, K.H., 1986. Bulk density. In: Klute, A. (Ed.), Methods of Soil Analysis. Part 1, Second ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI, pp. 363–375.
Bouma, J., 1989. Using soil survey data for quantitative land evaluation. Advances Soil Science 9: 177-213.
Garcia, L. A., Shigidi, A., 2006. Using neural networks for parameter estimation in ground water. Journal of Hydrology 318, 215–231.
Hillel, D., 2003. Introduction to Environmental soil physics.
Koekkok, E.J.W., Booltink, H., 1999. Neural network models to predict soil water retention. European Journal of Soil Science 50, 489–495.
Mehrotra, K., Mohan, C.K., Ranka, S., 1997. Elements of Artificial Neural Networks. The MIT Press, Boston. 344 pp.
Minasny, B., McBratney, A.B., 2002. The neuro-m methods for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66, 352–361.
Minasny .B., McBratney, A.B. 2004. Neural Networks Package for fitting Pedotransfer Functions. School of Land, Water & Crop Sciences, McMillan Building A05, the University of Sydney, NSW 2006, Australia.
Rumelhart, D.E., McClelland, J.L., 1998. Parallel Distributed Processing, Massachusetts Institute of Technology Press, Massachusetts, 547 pp., 1988.
Schaap, M.G., Leij, F.J., 1998. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil & Tillage Research 47, 37–42.
Schaap, M.G., Leij, F.J., van Genuchten, M.Th., 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal 62, 847–855.
Sparks, D.L., A. L. Page, P. A. Helmke, R. H. Leoppert, P. N. Soltanpour, M. A. Tabatabai, G. T. Johnston and M. E. Summer, 1996, Methods of soil analysis, Soil Science Society of America, Madison, Wisconsin.
Tamari, S., Wosten, J.H.M., Ruiz-Suarez, J.C., 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Science Society of America Journal 60, 1732–1741.
Abstract
The two common methods used to develop PTFs are multiple-linear regression method and Artificial Neural Network. One of the advantages of neural networks compared to traditional regression PTFs is that they do not require a priori regression model, which relates input and output data and in general is difficult because these models are not known. So at present research, we compare performance of feed-forward back-propagation network to predict soil properties. Soil samples were collected from different horizons profiles located in the Gorgan Province, North of Iran. Measured soil variables included texture, organic carbon, water saturation percentage Bulk density, Infiltration rate and deep percolation. Then, multiple linear regression and neural network model were employed to develop a pedotransfer function for predicting soil parameters using easily measurable characteristics of clay, silt, SP, Bd and organic carbon. The performance of the multiple linear regression and neural network model was evaluated using a test data set by R2, RMSE and RSE. Results showed that artificial neural network with two and five neurons in hidden layer had better performance in predicting soil hydraulic properties than multivariate regression. In conclusion, the result of this study showed that both ANN and regression predicted soil properties with relatively high accuracy that showed that strong relationship between input and output data and also high accuracy in determining of data.
Keywords: Infiltration rate, deep percolation, pedotransfer function
References
Amini, M., Abbaspour, K. C. Khademi., H. Fathianpour., N. Afyuni., M. Schulin. R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European Journal of Soil Science 56, 551 – 559.
Besaw, L.E. Rizzo D.M. Mouser, P.J., 2006. Application of an Artificial Neural Network for Analysis of Subsurface Contamination at the Schuyler Falls Landfill, 3rd Biennial meeting of the International Environmental Modelling and Software Society. July 9-13, 2006 The Wyndham Hotel, Burlington, Vermont, USA
Blake, G.R., Hartge, K.H., 1986. Bulk density. In: Klute, A. (Ed.), Methods of Soil Analysis. Part 1, Second ed. Agron. Monogr. 9. ASA and SSSA, Madison, WI, pp. 363–375.
Bouma, J., 1989. Using soil survey data for quantitative land evaluation. Advances Soil Science 9: 177-213.
Garcia, L. A., Shigidi, A., 2006. Using neural networks for parameter estimation in ground water. Journal of Hydrology 318, 215–231.
Hillel, D., 2003. Introduction to Environmental soil physics.
Koekkok, E.J.W., Booltink, H., 1999. Neural network models to predict soil water retention. European Journal of Soil Science 50, 489–495.
Mehrotra, K., Mohan, C.K., Ranka, S., 1997. Elements of Artificial Neural Networks. The MIT Press, Boston. 344 pp.
Minasny, B., McBratney, A.B., 2002. The neuro-m methods for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal, 66, 352–361.
Minasny .B., McBratney, A.B. 2004. Neural Networks Package for fitting Pedotransfer Functions. School of Land, Water & Crop Sciences, McMillan Building A05, the University of Sydney, NSW 2006, Australia.
Rumelhart, D.E., McClelland, J.L., 1998. Parallel Distributed Processing, Massachusetts Institute of Technology Press, Massachusetts, 547 pp., 1988.
Schaap, M.G., Leij, F.J., 1998. Using neural networks to predict soil water retention and soil hydraulic conductivity. Soil & Tillage Research 47, 37–42.
Schaap, M.G., Leij, F.J., van Genuchten, M.Th., 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal 62, 847–855.
Sparks, D.L., A. L. Page, P. A. Helmke, R. H. Leoppert, P. N. Soltanpour, M. A. Tabatabai, G. T. Johnston and M. E. Summer, 1996, Methods of soil analysis, Soil Science Society of America, Madison, Wisconsin.
Tamari, S., Wosten, J.H.M., Ruiz-Suarez, J.C., 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Science Society of America Journal 60, 1732–1741.