Data mining and exploration techniques

Developments in Soil Science

Volumn 30, Issue C, 2004, Pages 21-32

  Pachepsky, Ya ^a   Schaap, M G ^b  

^a BARC EAST   (United States)

^b U S SALINITY LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77956790215     PISSN: 01660918     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0166-2481(04)30002-4     Document Type: Review

References (37)

1. Alexander I., and Morton H. An Introduction to Neural Computing (1990), Chapman and Hall, London
2. Baker F.A. Classification and regression tree analysis for assessing hazard of pine mortality caused by Heterobasidion annosum. Plant Dis 77 (1993) 136-139
3. Barron A.R. Predicted Square Error: a Criterion for Automatic Model Selection. In: Farlow S.J. (Ed). Self-Organizing Methods in Modelling: GMDH-type Algorithms (1984), M. Dekker, New York, NY 87-104
4. Bell J.F. Tree-based methods. In: Fielding A.H. (Ed). Machine Learning Methods for Ecological Applications (1999), Kluwer Academic Publishers, Boston 84-111
5. Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. Regression Trees (1993), Chapman and Hall/CRC, Boca Raton, FL
6. Clark L.A., and Pregibon D. Tree-based models. In: Hastie T.J. (Ed). Statistical Models (1992), S. Wadsworth, Pacific Grove, California 377-419
7. Demuth H., and Beale M. Neural Network Toolbox Manual (1992), MathWorks Inc, Natick, Massachussetts
8. Efron B., and Tibshirani R.J. An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability (1993), Chapman and Hall, New York, NY
9. Farlow S.J. The GMDH algorithm. In: Farlow S.J. (Ed). Self-Organizing Methods in Modeling: GMDH-Type Algorithms (1984), M. Dekker, New York 1-24
10. Gimènez D., Rawls W.J., Pachepsky Y., and Watt J.P.C. Prediction of a pore distribution factor from soil textural and mechanical parameters. Soil Sci 166 (2001) 79-88
11. Good P.I. Resampling Methods: A Practical Guide to Data Analysis (1999), Birkhäuser, Boston
12. Haykin S. Neural Networks, a Comprehensive Foundation. 1st Ed. (1994), Macmillan College Publishing Company, New York, NY
13. Hecht-Nielsen R. Neurocomputing (1990), Addison-Wesley Publishing Company, Reading, MA
14. Koekkoek E., and Bootlink H. Development of a neural network model to predict soil water retention. Eur. J. Soil Sci 50 (1997) 489-495
15. Lees B.G., and Ritman K. Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environ. Manage 15 (1991) 823-831
16. Maren A.J., Harston C.T., and Pap R.M. Handbook of Neural Computing Applications (1990), Academic Press, San Diego
17. Mathsoft. SPLUS 2000 Professional. User's Manual (1999)
18. McCord N.M., and Illingworth W.T. A Practical Guide to Neural Nets (1990), Addison-Wesley, Reading, Mass
19. McKenzie N.J., and Jacquier D.W. Improving the Field Estimation of Saturated Hydraulic Conductivity in Soil Survey. Aust. J. Soil Res 35 (1997) 803-825
20. McKenzie N.J., and Ryan P.J. Spatial prediction of soil properties using environmental correlation. Geoderma 89 (1999) 67-94
21. Minasny B., McBratney A.B., and Bristow K.L. Comparison of different approaches to the development of pedotransfer functions for water-retention curves. Geoderma 93 (1999) 225-253
22. Pachepsky Y.A., and Rawls W.J. Accuracy and reliability of pedotransfer functions as affected by grouping soils. Soil Sci. Soc. Am. J 63 (1999) 1748-1757
23. Pachepsky Y.A., Timlin D., and Varallyay G. Artificial neural networks to estimate soil water retention from easily measurable data. Soil Sci. Soc. Am. J 60 (1996) 727-733
24. Pachepsky Y.A., Rawls W., Gimenez D., and Watt J.P.C. Use of soil penetration resistance and group method of data handling to improve soil water retention estimates. Soil and Tillage Research 49 (1998) 117-128
25. Pachepsky Y., Timlin D., and Ahuja L. Estimating saturated soil hydraulic conductivity using water retention data and neural networks. Soil Sci 164 (1999) 552-560
26. Rawls W.J., and Brakensiek D.L. Prediction of soil water properties for hydrologic modeling. In: Jones E.B., and Ward T.J. (Eds). Proceedings of the Symposium of Watershed Management in the Eighties. April 30-May 1, 1985, Denver, CO (1985), Am. Soc. Civil Eng, New York, NY 293-299
27. Rawls W.J., and Pachepsky Y.A. Soil consistence and structure as predictors of water retention. Soil Sci. Soc. Am. J 66 (2002) 1115-1126
28. Rawls W.J., and Pachepsky Y.A. Using field topographic descriptors to estimate soil water retention. Soil Sci 167 (2002) 235-243
29. Rummelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. In: Rummelhart D.E., and McClelland J.L. (Eds). Parallel Distributed Processing: Explorations in the Microstructure of Cognition Vol. I (1986), MIT Press, Cambridge, MA 318-362
30. Schaap M.G., and Bouten W. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res 32 (1996) 3033-3040
31. Schaap M.G., Leij F.L., and van Genuchten M.T. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Sci. Soc. Am. J 62 (1998) 847-855
32. Tamari S., Wösten J.H.M., and Ruiz-Suárez J.C. Testing an artificial neural network for predicting soil hydraulic conductivity. Soil Sci. Soc. Am. J 60 (1996) 1732-1741
33. Tomasella J., Pachepsky Y.A., Crestana S., and Rawls W.J. Comparison of two approximation techniques to develop pedotransfer functions of water retention of Brazilian soils. Soil Sci. Soc. Am. J 67 (2003) 1085-1092
34. Ungaro F., and Calzolari C. Using existing soil databases for estimating water-retention properties for soils of the Pianura Padano-Veneta region of North Italy. Geoderma 99 (2001) 99-121
35. Van Lanen H.A.J., van Diepen C.A.J., Reinds G.J., and de Koning G.H.J. A comparison of qualitative and quantitative physical land evaluations, using an assessment of the potential for sugar-beet growth in the European Community. Soil Use Manag 8 (1992) 80-89
36. Williams J., Prebble R.E., Williams W.T., and Hignett C.T. The influence of texture, structure and clay mineralogy on the soil moisture characteristic. Aust. J. Soil Res 21 (1983) 15-32
37. Wösten J.H.M., Pachepsky Y.A., and Rawls W.J. Pedotransfer functions: bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol 251 (2001) 123-150