Sensitivity analysis of the nonparametric nearest neighbor technique to estimate soil water retention

Vadose Zone Journal

Volumn 5, Issue 4, 2006, Pages 1222-1235

  Nemes, A ^a,b   Rawls, W J ^b   Pachepsky, Ya A ^b   Van Genuchten, M Th ^c  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

^c U S SALINITY LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SET; NEAREST NEIGHBOR ANALYSIS; PARAMETERIZATION; SENSITIVITY ANALYSIS; SOIL WATER; WATER RETENTION;

EID: 34547882350     PISSN: None     EISSN: 15391663     Source Type: Journal    
DOI: 10.2136/vzj2006.0017     Document Type: Article

References (39)

1. Baker, L. 2005. Optimisation of pedotransfer function models for soil hydraulic properties using an artificial neural network ensemble method. Ph.D. diss. Univ. of Abertay, Dundee, UK.
2. Dasarathy, B.V. (ed.). 1991. Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Comput. Soc. Press, Los Alamitos, CA.
3. Demuth, H., and M. Beale. 1992. Neural network toolbox manual. MathWorks, Natick, MA.
4. Efron, B., and R.J. Tibshirani. 1993. An introduction to the bootstrap. Monographs on statistics and applied probability. Chapman and Hall, New York.
5. Guber, A.K., Ya.A. Pachepsky, M.Th. van Genuchten, W.J. Rawls, J. Simunek, D. Jacques, T.J. Nicholson, and R.E. Cady. 2006. Field-scale water flow simulations using ensembles of pedotransfer functions for soil water retention. Vadose Zone J. 5:234-247.
6. Harrold, T.I., A. Sharma and S.J. Sheather. 2003a. A nonparametric model for stochastic generation of daily rainfall occurrence. Water Resour. Res. 39:1300. doi:10.1029/2003WR002182.
7. Harrold, T.I., A. Sharma and S.J. Sheather. 2003b. A nonparametric model for stochastic generation of daily rainfall amounts. Water Resour. Res. 39:1343. doi:10.1029/2003WR002570.
8. Haykin, S. 1994. Neural networks, a comprehensive foundation. Macmillan College Publ. Co., New York.
9. Hecht-Nielsen, R. 1990. Neurocomputing. Addison-Wesley, Reading, MA.
10. Houtemaker, P.L., L. Lefaivre, J. Derome, H. Ritchie, and H.L. Mitchell. 1996. A system simulation approach to ensemble prediction. Monthly Weather Rev. 124:1225-1242.
11. Jagtap, S.S., U. Lall, J.W. Jones, A.J. Gijsman, and J.T. Ritchie. 2004. Dynamic nearest-neighbor method for estimating soil water parameters. Trans. ASAE 47:1437-1444.
12. Koekkoek, E.J.W., and H. Booltink. 1999. Neural network models to predict soil water retention. Eur. J. Soil Sci. 50:489-495.
13. Lall, U., and A. Sharma. 1996. A nearest-neighbor bootstrap for resampling hydrologic time series. Water Resour. Res. 32:679-693.
14. MacLean, A.H., and T.U. Yager. 1972. Available water capacities of Zambian soils in relation to pressure plate measurements and particle size analysis. Soil Sci. 113:23-29.
15. Mehrotra, R., and A. Sharma. 2006. Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbour approach. Adv. Water Resour. 29:987-999.
16. Minasny, B., and A.B. McBratney. 2002. The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Sci. Soc. Am. J. 66:352-361.
17. Minasny, B., A.B. McBratney, and K.L. Bristow. 1999. Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma 93:225-253.
18. Molteni, F., R. Buizza, T.N. Palmer, and T. Petroliagis. 1996. The ECMWFensemble prediction system: Methodology and validation. Q. J. R. Meteorol. Soc. 122:73-119.
19. Nemes, A., W.J. Rawls, and Ya.A. Pachepsky. 2006. Use of a nonparametric nearest-neighbor technique to estimate soil water retention. Soil Sci. Soc. Am. J. 70:327-336.
20. Nemes, A., M.G. Schaap, and J.H.M. Wösten. 2003. Functional evaluation of pedotransfer functions derived from different scales of data collection. Soil Sci. Soc. Am. J. 67:1093-1102.
21. Nemes, A., J.H.M. Wösten, A. Lilly, and J.H. Oude Voshaar. 1999. Evaluation of different procedures to interpolate the cumulative particle-size distribution to achieve compatibility within a soil database. Geoderma 90:187-202.
22. Pachepsky, Ya.A., D. Timlin, and G. Várallyay. 1996. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci. Soc. Am. J. 60:727-773.
23. Palmer, T.N., A. Alessandri, U. Andersen, P. Cantelaube, M. Davey, P. Délécluse, M. Déqué, E. Díez, F.J. Doblas-Reyes, H. Feddersen, R. Graham, S. Gualdi et al. 2004. Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER). Bull. Am. Meteorol. Soc. 85:853-872.
24. Schaap, M.G., and F.J. Leij. 1998. Database-related accuracy and uncertainty of pedotransfer functions. Soil Sci. 163:765-779.
25. Schaap, M.G., and F.J. Leij. 2000. Improved prediction of unsaturated hydraulic conductivity with the Mualem-van Genuchten model. Soil Sci. Soc. Am. J. 64:843-851.
26. Schaap, M.G., F.J. Leij, and M.Th. van Genuchten. 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci. Soc. Am. J. 62:847-855.
27. Schaap, M.G., F.J. Leij, and M.Th. van Genuchten. 1999. A bootstrap-neural network approach to predict soil hydraulic parameters. p. 1237-1250. In M.Th. van Genuchten et al. (ed.) Proc. Int. Worksh., Characterization and Measurements of the Hydraulic Properties of Unsaturated Porous Media, Riverside, CA. 22-24 Oct. 1997. Univ. of California, Riverside.
28. Sharma, A., and R. O'Neill. 2002. A nonparametric approach for representing interannual dependence in monthly streamflow sequences. Water Resour. Res. 38:5. doi:10.1029/2001WR000953.
29. Soil Survey Staff. 1951. Soil survey manual. U.S. Dep. Agric. Handbook no. 18. U.S. Gov. Print. Office, Washington, DC.
30. Soil Survey Staff. 1997. National characterization data. Soil Survey Lab., Natl. Soil Survey Center, Lincoln, NE.
31. Tamari, S., J.H.M. Wösten, and J.C. Ruiz-Suárez. 1996. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J. 60:771-774.
32. Tarboton, D.G., A. Sharma, and U. Lall. 1998. Disaggregation procedures for stochastic hydrology based on nonparametric density estimation. Water Resour. Res. 34:107-119.
33. Tomasella, J., M.G. Hodnett, and L. Rossato. 2000. Pedotransfer functions for the estimation of soil water retention in Brazilian soils. Soil Sci. Soc. Am. J. 64:327-338.
34. Tomasella, J., Ya.A. Pachepsky, S. Crestana, and W.J. Rawls. 2003. Comparison of two techniques to develop pedotransfer functions for water retention. Soil Sci. Soc. Am. J. 67:1085-1092.
35. Wettschereck, D., D.W. Aha, and T. Mohri. 1997. A review and empirical evaluation of feature weighting methods for a class of lazy learning algorithms. Artif. Intell. Rev. 11(1-5):273-314.
36. Wösten, J.H.M., A. Lilly, A. Nemes, and C. Le Bas. 1999. Development and use of a database of hydraulic properties of European soils. Geoderma 90:169-185.
37. Yakowitz, S. 1993. Nearest-neighbor estimation for null-recurrent Markov time series. Stoch. Proc. Appl. 48:311-318.
38. Yates, D., S. Gangopadhyay, B. Rajagopalan, and K. Strzepek. 2003. A technique for generating regional climate scenarios using a nearest-neighbor algorithm. Water Resour. Res. 39(7):1199. doi:10.1029/2002WR001769.
39. Ye, M., S.P. Neuman, and P.D. Meyer. 2004. Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff. Water Resour. Res. 40:W05113. doi:10.1029/2003WR002557.