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Comparison of Three Regression Models for Determining Water Retention Curves Cover

Comparison of Three Regression Models for Determining Water Retention Curves

Journal of Hydrology and Hydromechanics
Volume 59 (2011): Issue 4 (December 2011)
By: ,   and    
Open Access
|Nov 2011
References

References

  1. BOUMA J., 1989: Using Soil Survey Data for Quantitative Land Evaluation. Adv. Soil Sci., 9, 177-213.
    Open DOISearch in Google Scholar
  2. BOUMA J., VAN LANEN A. J., 1987: Transfer Function and Treshhold Values: From Soil Characteristics to Land Qualities. K. J. Beek, et al. (eds.) Quantified land evaluation. Proc Worksh. ISSS and SSSA, Washington, D. C. 106-110.
    Search in Google Scholar
  3. CHANG C. C. and LIN C. J., 2001: LIBSVM: A Library for Support Vector Machines (2001). (Version 2.91, April 2010). Software, available at:
    Search in Google Scholar
  4. ČISTÝ M.: Application of the Harmony Search Optimization in Irrigation. In: Recent advances in harmony search algorithm. Berlin: Springer Verlag London, 2010. ISBN 978-3-642-04316-1, 123-134.
    Search in Google Scholar
  5. GEEM Z. W., KIM J. H. and LOGANATHAN G. V., 2001: A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76, 60-68.
    Open DOISearch in Google Scholar
  6. GUPTA S. C., LARSON W. E., 1979: Estimating soil water retention characteristics from particle size distribution, organic matter percentage, and bulk density. Water Resour. Res., 15, 1633-1635.
    Open DOISearch in Google Scholar
  7. KUMAR B., SREENIVASULU G., RAMAKRISHNA A. RAO, 2010: Radial Basis Function Network Based Design of Incipient Motion Condition of Alluvial Channels with Seepage. J. Hydrol. Hydromech.,, 2010, 2, 102-113.
    Open DOISearch in Google Scholar
  8. MINASNY B., MCBRATNEY A. B., 2002: The neuro-m methods for fitting neural network parametric pedotransfer function. Soil Sci. Soc. Am. J., 66, 352-361.
    Open DOISearch in Google Scholar
  9. MINASNY B., MCBRATNEY A. B., BRISTOW K. L., 1999: Comparison of different approaches to the development of pedotransfer functions for water retention curves. Geoderma, 93, 225-253.
    Open DOISearch in Google Scholar
  10. PACHEPSKY YA. A., TIMLIN D. J. and VARALLYAY G., 1996: Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci. Soc. Am. J., 60, 727-733.
    Open DOISearch in Google Scholar
  11. RAWLS W. J., BRAKENSIEK D. L., SAXTON K. E., 1982: Estimating soil water retention properties. Trans. ASAE, 25, 1316-1320.
    Open DOISearch in Google Scholar
  12. RUMELHART D. E., HINTON G. E., WILLIAMS R. J., 1986: Learning internal representation by error propagation. Rumelhart D. E. & McClelland J. L. (eds.): Parallel distributed processing: explorations in the microstructure of cognition, Vol., Cambridge MA, MIT Press, pp. 318-362.
    Open DOISearch in Google Scholar
  13. SCHAAP M. G., LEIJ F.J, VAN GENUCHTEN M. Th., 1998: Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci. Soc. Am. J., 62, 847-855.
    Open DOISearch in Google Scholar
  14. SKALOVÁ J., 2001: Pedotransfer functions of the Záhorská nížina soils and their application to soil-water regime modeling. (In Slovak.) Faculty of Civil Engineering STU Bratislava, 112 pp.
    Search in Google Scholar
  15. SKALOVÁ J., JAROŠ B., NOVÁK V., 2009: The Influence of Different Canopies on Groundwater Table Level Changes at Kláštorské Lúky Natural Reserve. J. Hydrol. Hydromech.,, 2009, 4, 276-285.
    Open DOISearch in Google Scholar
  16. ŠTEKAUEROVÁ V., SKALOVÁ J., 1999: Calculation of the drying brunch of water retention curves from easily measured soil properties. (In Slovak.) VII. Poster Day. UH SAS Bratislava, 133-134.
    Search in Google Scholar
  17. ŠÚTOR J., ŠTEKAUEROVÁ V., 1999: Determination of the water retention curve points from the basic physical characteristics of soil. Influence of anthropogenic activity for water regime of plain area. (In Slovak.) ÚH SAV, Michalovce, 151-157.
    Search in Google Scholar
  18. TAMARI S., WOSTEN J. H. M. and RUIZ-SUAREZ J. C., 1996: Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J., 60, 1732-1741.
    Open DOISearch in Google Scholar
  19. VAPNIK V., 1995: The Nature of Statistical Learning Theory. Springer, NY.
    Open DOISearch in Google Scholar
  20. VAPNIK V., 1998: Statistical Learning Theory. Wiley, NY.
    Search in Google Scholar
  21. WÖSTEN J. H. M., PACHEPSKY A.YA., RAWLS W. J., 2001: Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol, 251, 123-150.
    Open DOISearch in Google Scholar
DOI: https://doi.org/10.2478/v10098-011-0023-7 | Journal eISSN: 1338-4333 (formerly 0042-790X) | Journal ISSN: 0042-790X
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Language: English, Czech, Slovak
Page range: 275 - 284
Published on: Nov 30, 2011
Published by: Slovak Academy of Sciences, Institute of Hydrology
In partnership with: Paradigm Publishing Services
Keywords:
Soil-Water Retention Curve,
Pedotransfer Function,
Neural Networks,
Support Vector Machines,
Multiple Linear Regression
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2011 Jana Skalová, Milan Čistý, Juraj Bezák, published by Slovak Academy of Sciences, Institute of Hydrology
This work is licensed under the Creative Commons License.

Volume 59 (2011): Issue 4 (December 2011)
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