Comparison of Three Regression Models for Determining Water Retention Curves

BOUMA J., 1989: Using Soil Survey Data for Quantitative Land Evaluation. Adv. Soil Sci., 9, 177-213.10.1007/978-1-4612-3532-3_4Search in Google Scholar

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

CHANG C. C. and LIN C. J., 2001: LIBSVM: A Library for Support Vector Machines (2001). (Version 2.91, April 2010). Software, available at: http://www.csie.ntu.edu.tw/~cjlin/libsvmSearch in Google Scholar

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

GEEM Z. W., KIM J. H. and LOGANATHAN G. V., 2001: A New Heuristic Optimization Algorithm: Harmony Search. Simulation, 76, 60-68.10.1177/003754970107600201Search in Google Scholar

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, 1633-1635.10.1029/WR015i006p01633Search in Google Scholar

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., 58, 2010, 2, 102-113.10.2478/v10098-010-0010-4Search in Google Scholar

MINASNY B., MCBRATNEY A. B., 2002: The neuro-m methods for fitting neural network parametric pedotransfer function. Soil Sci. Soc. Am. J., 66, 352-361.10.2136/sssaj2002.3520Search in Google Scholar

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, 225-253.10.1016/S0016-7061(99)00061-0Search in Google Scholar

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, 727-733.10.2136/sssaj1996.03615995006000030007xSearch in Google Scholar

RAWLS W. J., BRAKENSIEK D. L., SAXTON K. E., 1982: Estimating soil water retention properties. Trans. ASAE, 25, 1316-1320.10.13031/2013.33720Search in Google Scholar

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. 1, Cambridge MA, MIT Press, pp. 318-362.10.7551/mitpress/5236.001.0001Search in Google Scholar

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, 847-855.10.2136/sssaj1998.03615995006200040001xSearch in Google Scholar

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

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., 57, 2009, 4, 276-285.10.2478/v10098-009-0024-ySearch in Google Scholar

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

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

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, 1732-1741.10.2136/sssaj1996.03615995006000060018xSearch in Google Scholar

VAPNIK V., 1995: The Nature of Statistical Learning Theory. Springer, NY.10.1007/978-1-4757-2440-0Search in Google Scholar

VAPNIK V., 1998: Statistical Learning Theory. Wiley, NY.Search in Google Scholar

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, 123-150.10.1016/S0022-1694(01)00464-4Search in Google Scholar