Using neural networks to predict soil water retention and soil hydraulic conductivity

Soil and Tillage Research

Volumn 47, Issue 1-2, 1998, Pages 37-42

  Schaap, Marcel G ^a   Leij, Feike J ^a  

^a U S SALINITY LABORATORY   (United States)

Author keywords

Hydraulic conductivity; Neural networks; Pedotransfer function; Water retention

Indexed keywords

ARTIFICIAL NEURAL NETWORK; HYDRAULIC CONDUCTIVITY; PEDOTRANSFER FUNCTION; SOIL WATER; WATER RETENTION;

EID: 0032474481     PISSN: 01671987     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-1987(98)00070-1     Document Type: Conference Paper

References (20)

1. Ahuja L.R., Cassel D.K., Bruce R.R., Barnes B.B. Evaluation of spatial distribution of hydraulic conductivity using effective porosity data. Soil Sci. 148:1989;404-411.
2. Efron, B., Tibshirani, R.J., 1993. An Introduction to the bootstrap. Monographs on statistics and applied probability. Chapman and Hall, New York, NY.
3. Gardner W.R. Some steady-state solutions of the unsaturated moisture flow equations with application to evaporation from a water table. Soil Sci. 85:1958;228-232.
4. Gupta S.C., Larson W.E. Estimating soil water characteristics from particle size distribution, organic matter percent, and bulk density. Water Resour. Res. 15:1979;1633-1635.
5. Haykin, S, 1994. Neural Networks, a comprehensive foundation, 1st ed. Macmillan College Publishing Company, New York, NY.
6. Kern J.S. Evaluation of soil water retention models based on basic soil physical properties. Soil Sci. Soc. Am. J. 59:1995;1134-1141.
7. Leij, F.J., Alves, W.J., van Genuchten, M. Th., Williams, J.R., 1996. The unsoda unsaturated soil hydraulic database, version 1.0. EPA report EPA/600/R-96/095, EPA National Risk Management Laboratory, G-72, Cincinnati, OH, USA. (http://www.epa.gov/ada/models.html).
8. Mualem Y. A new model predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 12:1976;513-522.
9. Nelder J.A., Mead R. A simplex method for function minimization. Computer J. 7:1965;308-313.
10. Pachepsky Ya.A., Timlin D., Varallyay G. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci. Soc. Am. J. 60:1996;727-733.
11. Rawls W.J., Brakensiek D.L., Saxton K.E. Estimation of soil water properties. Trans. ASAE. 25:1982;1316-1320.
12. Schaap M.G., Bouten W. Modelling water retention curves of sandy soils using neural networks. Water Resour. Res. 32:1996;3033-3040.
13. Schaap, M.G., Leij, F.J., van Genuchten, M.Th., 1998. Neural network analysis for hierarchical prediction of soil water retention and saturated hydraulic conductivity. Soil Sci. Soc. Am. J. submitted.
14. Schaap, M.G., Leij, F.J., 1998. A comparison of methods to predict unsaturated hydraulic conductivity. Water Resour. Res. submitted.
15. Tamari S., Wösten J.H.M., Ruiz-Suárez J.C. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J. 60:1996;1732-1741.
16. Tietje O., Hennings V. Accuracy of the saturated hydraulic conductivity prediction by pedo-transfer functions compared to the variability within FAO textural classes. Geoderma. 69:1996;71-84.
17. Tietje O., Tapkenhinrichs M. Evaluation of pedotransfer functions. Soil Sci. Soc. Am. J. 57:1993;1088-1095.
18. van Genuchten M.Th. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Sci. Am. J. 44:1980;892-898.
19. Vereecken H., Maes J., Feyen J., Darius P. Estimating the soil moisture retention characteristic from texture, bulk density, and carbon content. Soil Sci. 148:1989;389-403.
20. Vereecken H., Maes J., Feyen J. Estimating unsaturated hydraulic conductivity from easily measured soil properties. Soil Sci. 149:1990;1-12.