Radial tree-growth modelling with fuzzy regression

Canadian Journal of Forest Research

Volumn 28, Issue 8, 1998, Pages 1249-1260

  Boreux, J J ^a   Gadbin Henry, C ^b   Guiot, J ^b   Tessier, L ^b  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b AIX MARSEILLE UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY MATHEMATICS; GROWTH; MODELING; TREE;

PINUS PINEA;

EID: 0032424831     PISSN: 00455067     EISSN: None     Source Type: Journal    
DOI: 10.1139/x98-088     Document Type: Article

References (42)

1. Badeau, V., Dupouey, J.L., Becker, M., and Picard, J.F. 1995. Long-term growth trends of Fagus sylvatica L. in northeastern France. A comparison between high and low density stands. Acta Oecol. 16: 571-583.
2. Bardossy, A., Bogardi, I., and Duckstein, L. 1990. Fuzzy regression in hydrology. Water Resour. Res. 26: 1497-1508.
3. Bardossy, A., Duckstein, L., and Bogardi, I. 1993. Fuzzy non-linear regression analysis of dose response relationship. Eur. J. Oper. Res. 66: 36-51.
4. Berger, J.O. 1985. Statistical decision theory and Bayesian analysis. Springer-Verlag, New York.
5. Bernier, J. 1987. Elements of Bayesian analysis of uncertainty in hydrological reliability and risk models. In Engineering reliability and risk in water resources. Edited by L. Duckstein and E. Plate. Martinus Nijhoff, Boston, Mass. pp. 405-421.
6. Bert, G.D. 1992. Influence du climat, des facteurs stationnels et de la pollution sur la croissance et l'état sanitaire du sapin pectiné (Abies alba) dans le jura. Ph.D. thesis, Université de Nancy, Nancy, France.
7. Boreux, J.J., Pesti, G., Nicolas, J., and Duckstein, L. 1997. Age model estimation in paleoclimatic research: fuzzy regression and radiocarbon uncertainties. Palaeogeogr. Palaeoclim. Palaeoecol, 128: 29-37.
8. Burman, R., and Pochop, L. 1994. Evaporation, evapotranspiration and climatic data. Dev. Atmos. Sci. No. 22.
9. Caselton, W.F., and Luo, W. 1994. Inference and decision under near ignorance conditions. Engineering risk in natural resources management with special references to hydrosystems under changes of physical or climatic environment. Edited by L. Duckstein and E. Parent. Kluwer Academic Publishers, Dordrecht, the Netherlands. pp. 291-303.
10. Chu, C., and Marron, J. 1991. Choosing a kernel regression estimator (with discussion). Stat. Sci. 6: 404-436.
11. Cropper, J.P. 1982. Comment on response functions. In Climate from tree-rings. Edited by M.K. Hughes, P.M. Kelly, J.R. Pilcher, V.C. LaMarche Jr. Cambridge University Press, Cambridge, U.K. pp. 38-45.
12. Douglass, A.E. 1936. Climatic cycles and tree-growth. Vol. 3. A study of cycle. Carnagie Inst. Wash. Publ. No. 289.
13. Dubois, D., and Prade, H. 1980. Fuzzy sets and system theory and applications. Academic Press, San Diego, Calif.
14. Efron, B. 1979. Bootstrap methods: another look at the jackknife. Ann. Stat. 7: 1-26.
15. Efron, B. 1983. Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78: 316-331.
16. Emberger, L. 1930. La végétation de la région méditerranéenne. Essai d'une classification des groupements végétaux. Rev. Gen. Bot. 42: 641-662 and 705-721.
17. Fan, J., and Gijbels, I. 1982. Local polynomial modelling and its applications. Chapman & Hall, London, U.K.
18. Federer, C.A. 1982. Transpirational supply and demand: plant, soil, and atmospheric effects evaluated by simulation. Water Resour. Res. 18: 355-362.
19. Fritts, H.C. 1976. Tree-rings and climate. Academic Press, London, U.K.
20. Fritts, H.C. 1982. The climate growth response. In Climate from tree-rings. Edited by M.K. Hughes, P.M. Kelly, J.R. Pilcher, V.C. LaMarche Jr. Cambridge University Press, Cambridge, U.K. pp. 38-45.
21. Fritts, H.C., Blasing, T.J., Hayden, B.P. and Kutzbach, J.E. 1971. Multivariate techniques for specifying tree-growth and climate relationships and for reconstructing anomalies in paleoclimate. J. Appl. Meteorol. 10: 845-864.
22. Gadbin-Henry, C. 1994. Études dendroécologiques de Pinus pinea L. Aspects méthodologiques. Ph.D. thesis, Université d'Aix-Marseille III, Aix-en-Provence, France.
23. Guiot, J. 1989. Method of calibration and comparison of methods. In Methods of dendrochronology. Edited by E.R Cook. and L.A Kairiukstis. Kluwer Academic Publishers and International Institute for Applied Systems Analysis, Dordrecht, the Netherlands. pp. 165-178 and 185-193.
24. Guiot, J., and Tessier, L. 1997. Detection of pollution signals in tree-ring series using AR processes and neural networks. In Applications of time-series analysis in astronomy and meteorology. Edited by T.S. Rao, M.B. Priestley, and O. Lessi. Chapman & Hall, London, U.K. pp. 413-426.
25. Guiot, J., Berger, A.L., and Munaut, A.V. 1982a. Response functions. In Climate from tree-rings. Edited by M.K. Hughes, P.M. Kelly, J.R. Pilcher, V.C. LaMarche Jr. Cambridge University Press, Cambridge, U.K. pp. 38-45.
26. Guiot, J., Berger, A.L. Munaut, A.V., and Till, C. 1982b. Some new mathematical procedures in dendroclimatology with examples for Switzerland and Morocco. Tree-Ring Bull. 42: 33-48.
27. Harrison, S.P., Prentice, I.C., and Guiot, J. 1993. Climatic controls of Holocene lake-level changes in Europe. Clim. Dyn. 8: 189-200.
28. Hastie, T., and Loader, C. 1993. Local regression: automatical kernel carpentry (with discussion). Stat. Sci. 8: 120-143.
29. Heshmaty, B., and Kandel, A. 1985. Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy Sets Syst, 15: 159-191.
30. Jarvis, P.G., and MacNaughton, K.G. 1986: Stomatal control of transpiration: scaling up from leaf to region. Adv. Ecol. Res. No. 15. pp. 1-49.
31. Kaufmann, A., and Gupta, M.M. 1991. Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New York.
32. Lebourgeois, F. 1995. Étude dendroécologique et écophysiologique du Pin laricio de Corse (Pinus nigra A. ssp. laricio) en région pays de Loire. Ph.D. thesis, Université de Paris-Sud, Orsay, Paris, France.
33. Neter, J., Wasserman, W., and Kutner, M.H. 1989. Applied linear regression models. 2nd ed. Irwin, Homewood, III.
34. Prentice, I.C., Cramer, W., Harrison, S.P., Leemans, R., and Monserud, R.A. 1992. A global biome model based on plant physiology and dominance, soil properties and climate. J. Biogeogr. 19: 117-134.
35. Prentice, I.C., Sykes, M.T., and Cramer, W. 1993. A simulation model for the transient effects of climate change on forest landscapes. Ecol. Modell. 65: 51-71.
36. Redden, D.T., and Woodall, H. 1996. Further examination of fuzzy linear regression. Fuzzy Sets Syst. 79: 203-211.
37. Schweingruber, F.H., Kairiukstis, L., and Shiyatov, S. 1990. Sample selection. In Methods of dendrochronology. Edited by E.R Cook and L.A Kairiukstis. Kluwer Academic Publishers and IIASA, Dordrecht, the Netherlands. pp. 23-96.
38. Shashkin, A.V., and Fritts, H.C. 1995. A model that simulates cambial activity and ring structure in conifers. In Tree rings, from the past to the future. Edited by S. Ohta and M.K. Hughes. Forestry and Forest Products Research Institute, Tsukuba, Japan. pp. 24-30.
39. Tanaka, H., Uejima, S., and Asai, K. 1982. Linear regression analysis with fuzzy model. IEEE Trans. Syst. Man Cybern. 12: 903-907.
40. Viertl, R. 1997. Non-precise information in Bayesian inference. In Statistical and Bayesian methods in hydrological sciences. Edited by E. Parent and B. Bobie. UNESCO Press, Paris. pp. 465-478.
41. Zadeh, L.A. 1965. Fuzzy sets. Inf. Control, 8: 338-353.
42. Zadeh, L.A. 1973. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man. Cybern. 3: 28-44.