SWS Academic Research eLibraryEarth & Planetary Sciences

Scholarly record

MATHEMATICAL MODELS OF UNCERTAINTY FOR SURFACE ANALYSES AND DECISION MAKING

Caha, Jan, Vondrбkovб, Alena, Dvorskэ, Jiri

First published: 2013-06-20https://doi.org/10.5593/sgem2013/bb2.v1/s11.021View metrics

Publication Impact Profile

PlumX
  • Citations
  • Scopus - Citation Indexes: 2
  • Captures
  • Mendeley - Readers: 4
PLUMX see details

Publication details

Title
MATHEMATICAL MODELS OF UNCERTAINTY FOR SURFACE ANALYSES AND DECISION MAKING
Authors
Caha, Jan, Vondrбkovб, Alena, Dvorskэ, Jiri
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 13th SGEM GeoConference on INFORMATICS, GEOINFORMATICS AND REMOTE SENSING
Publisher
Stef92 Technology
Year
2013
Pages
773 - 780 pp
ISSN
1314-2704
ISBN
Not available yet
Language
en
Publication type
Conference Paper
References46

  1. BROWN, J. D. & HEUVELINK, G.B.MThe Data Uncertainty Engine (DUE): A software tool for assessing and simulating uncertain environmental variables. Computers & Geosciences. 2007, Vol. 33, no. 2, pp. 172– 190.

  2. CROSETTO, M., TARANTOLA, S. & SALTELLI, A. Sensitivity and uncertainty analysis in spatial modelling based on GIS. In: Agriculture Ecosystems & Environment. 2000, Vol. 81, no. 1, pp. 71– 79.

  3. DUBOIS, D. & PRADE, H. Possibility Theory An approach to Computerized Processing of Uncertainty. New York: Plenum Press, 1986.

  4. FISHER, P. F. & TATE, N. J. Causes and consequences of error in digital elevation models. Progress in Physical Geography. 2006, Vol. 30, no. 4, pp. 467–489.

  5. FISHER, P. Improved Modeling of Elevation Error with Geostatistics. GeoInfomatica. 1998, Vol. 2, no. 3, pp. 215– 233.

  6. HANSS, M. Applied fuzzy arithmetic : an introduction with engineering applications. Berlin ; New York: Springer-Verlag, 2005.

  7. HEUVELINK, G. B. M. Analysing Uncertainty Propagation in GIS:Why is it not that Simple In: FOODY, G. M. and ATKINSON, P. M. (eds.), Uncertainty in remote sensing and GIS. Chichester: Wiley, 2002. pp. 307. GeoConference on Informatics, Geoinformatics and Remote Sensing

  8. JANOŠKA, Z. & DVORSKÝ, J. P systems: State of the art with respect to representation of geographical space. In: CEUR Workshop Proceedings. S.l.: s.n., 2012. pp. 13–24.

  9. KALOS, M. H. & WHITLOCK, P. A. Monte Carlo Methods. Weinheim, Germany: WILEY-VCH Verlag GmbH & Co. KGaA, 204.

  10. LODWICK, W., ANILE, M. & SPINELLA, S. Introductio n. In: LODWICK, W. (ed.), Fuzzy surfaces in GIS and geographical analysis : theory, analytical methods, algorithms, and applications. Boca Raton: CRC Press, 2008. pp. 1–46.

  11. LOQUIN, K. & DUBOIS, D. Kriging with Ill -Known Variogram and Data. In: DESHPANDE, Amol and HUNTER, Anthony (eds.), Scalable Uncertainty Management. S.l.: Springer Berlin / Heidelberg, 2010. pp. 219–235.

  12. LONGLEY, P., GOODCHILD, M. F., MAGUIRE, D. & RHIND, D. Geographical information systems and science. 2nd. Chichester: Wiley, 2005.

  13. MacEACHREN, A. M., ROBINSON, A., HOPPER, S., GARDNER, S., MURRAY, R., GAHEGAN, M. & HETZLER, E. Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know. Cartography and Geographic Information Science . 2005. Vol. 32, no. 3, pp. 139– 160.

  14. MILLER, C. L. & LAFLAMME, R. A. The digital terrain model - theory & application. Cambridge, Mass.: M.I.T. Photogrammetry Laboratory, 1958.

  15. OBERGUGGENBERGER, M. The mathematics of uncertainty: models, methods and interpretations. In: FELLIN, W., LESSMANN, H., OBERGUGGENBERGER, M. and VIEIDER, R. (eds.), Analyzing Uncertainty in Civil Engineering. Berlin: Springer, 2005. pp. 252.

  16. OKSANEN, J. & SARJAKOSKI, T. Error propagation of DEM -based surface derivatives. Computers & Geosciences. 2005, Vol. 31, no. 8, pp. 1015– 1027.

  17. SANTOS, J., LODWICK, W. A. & NEUMAIER, A. A New Approach to Incorporate Uncertainty in Terrain Modeling. In: EGENHOFER, M. J. and MARK, D. M. (eds.), Geographic Information Science. Springer Berlin H eidelberg, 2002. pp. 291– 299.

  18. SHI, W. Principles of modeling uncertainties in spatial data and analyses. Boca Raton: CRC Press/Taylor & Francis, 2010. ISBN 9781420059274.

  19. SUGUMARAN, R. & DEGROOTE, J. Spatial decision support systems : principles and practices. Boca Raton: Taylor & Francis, 2011.

  20. VIERTL, R. Statistical methods for fuzzy data. Chichester, West Sussex: Wiley, 2011.

  21. ZADEH, L. A. Generalized theory of uncertainty (GTU) - principal concepts and ideas. Computational Statistics & Data Analysis. 2006, Vol. 51, no. 1, pp. 15–46.

  22. ZADEH, L. A. Fuzzy Sets. In: Information and Control. 1965, Vol. 8, no. 3, pp. 338– 353.

  23. ZHANG, J. & GOODCHILD, M. F. Uncertainty in geographical information. London: Taylor & Francis, 2002.

  24. BROWN, J. D. & HEUVELINK, G.B.MThe Data Uncertainty Engine (DUE): A software tool for assessing and simulating uncertain environmental variables. Computers & Geosciences. 2007, Vol. 33, no. 2, pp. 172– 190.

  25. CROSETTO, M., TARANTOLA, S. & SALTELLI, A. Sensitivity and uncertainty analysis in spatial modelling based on GIS. In: Agriculture Ecosystems & Environment. 2000, Vol. 81, no. 1, pp. 71– 79.

  26. DUBOIS, D. & PRADE, H. Possibility Theory An approach to Computerized Processing of Uncertainty. New York: Plenum Press, 1986.

  27. FISHER, P. F. & TATE, N. J. Causes and consequences of error in digital elevation models. Progress in Physical Geography. 2006, Vol. 30, no. 4, pp. 467–489.

  28. FISHER, P. Improved Modeling of Elevation Error with Geostatistics. GeoInfomatica. 1998, Vol. 2, no. 3, pp. 215– 233.

  29. HANSS, M. Applied fuzzy arithmetic : an introduction with engineering applications. Berlin ; New York: Springer-Verlag, 2005.

  30. HEUVELINK, G. B. M. Analysing Uncertainty Propagation in GIS:Why is it not that Simple In: FOODY, G. M. and ATKINSON, P. M. (eds.), Uncertainty in remote sensing and GIS. Chichester: Wiley, 2002. pp. 307. GeoConference on Informatics, Geoinformatics and Remote Sensing

  31. JANOŠKA, Z. & DVORSKÝ, J. P systems: State of the art with respect to representation of geographical space. In: CEUR Workshop Proceedings. S.l.: s.n., 2012. pp. 13–24.

  32. KALOS, M. H. & WHITLOCK, P. A. Monte Carlo Methods. Weinheim, Germany: WILEY-VCH Verlag GmbH & Co. KGaA, 204.

  33. LODWICK, W., ANILE, M. & SPINELLA, S. Introductio n. In: LODWICK, W. (ed.), Fuzzy surfaces in GIS and geographical analysis : theory, analytical methods, algorithms, and applications. Boca Raton: CRC Press, 2008. pp. 1–46.

  34. LOQUIN, K. & DUBOIS, D. Kriging with Ill -Known Variogram and Data. In: DESHPANDE, Amol and HUNTER, Anthony (eds.), Scalable Uncertainty Management. S.l.: Springer Berlin / Heidelberg, 2010. pp. 219–235.

  35. LONGLEY, P., GOODCHILD, M. F., MAGUIRE, D. & RHIND, D. Geographical information systems and science. 2nd. Chichester: Wiley, 2005.

  36. MacEACHREN, A. M., ROBINSON, A., HOPPER, S., GARDNER, S., MURRAY, R., GAHEGAN, M. & HETZLER, E. Visualizing Geospatial Information Uncertainty: What We Know and What We Need to Know. Cartography and Geographic Information Science . 2005. Vol. 32, no. 3, pp. 139– 160.

  37. MILLER, C. L. & LAFLAMME, R. A. The digital terrain model - theory & application. Cambridge, Mass.: M.I.T. Photogrammetry Laboratory, 1958.

  38. OBERGUGGENBERGER, M. The mathematics of uncertainty: models, methods and interpretations. In: FELLIN, W., LESSMANN, H., OBERGUGGENBERGER, M. and VIEIDER, R. (eds.), Analyzing Uncertainty in Civil Engineering. Berlin: Springer, 2005. pp. 252.

  39. OKSANEN, J. & SARJAKOSKI, T. Error propagation of DEM -based surface derivatives. Computers & Geosciences. 2005, Vol. 31, no. 8, pp. 1015– 1027.

  40. SANTOS, J., LODWICK, W. A. & NEUMAIER, A. A New Approach to Incorporate Uncertainty in Terrain Modeling. In: EGENHOFER, M. J. and MARK, D. M. (eds.), Geographic Information Science. Springer Berlin H eidelberg, 2002. pp. 291– 299.

  41. SHI, W. Principles of modeling uncertainties in spatial data and analyses. Boca Raton: CRC Press/Taylor & Francis, 2010. ISBN 9781420059274.

  42. SUGUMARAN, R. & DEGROOTE, J. Spatial decision support systems : principles and practices. Boca Raton: Taylor & Francis, 2011.

  43. VIERTL, R. Statistical methods for fuzzy data. Chichester, West Sussex: Wiley, 2011.

  44. ZADEH, L. A. Generalized theory of uncertainty (GTU) - principal concepts and ideas. Computational Statistics & Data Analysis. 2006, Vol. 51, no. 1, pp. 15–46.

  45. ZADEH, L. A. Fuzzy Sets. In: Information and Control. 1965, Vol. 8, no. 3, pp. 338– 353.

  46. ZHANG, J. & GOODCHILD, M. F. Uncertainty in geographical information. London: Taylor & Francis, 2002.

View or Download full articleAccess options
Full paper accessChoose SWS login, librarian support, or instant article download.

SWS access login

Login as SWS Scientific Committee

Authors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.

For librarian assistance: [email protected]

Purchase Instant Access

48-hour online accessComing soon
Online-only accessComing soon
Download the full article in PDF formatEUR 35
  • Article can be downloaded after successful payment.
  • Article may be used according to SWS library access terms.
  • Article cannot be redistributed.
Get full paper

Back to publication list