Skip to main content
SpringerLink
Log in

An Approach for Imperfection Propagation: Application to Land Cover Change Prediction

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8467))

Included in the following conference series:

Abstract

We propose an approach that propagates imperfection throu- [2]ghout a model of land cover change prediction. The proposed approach is based on Polynomial Collocation method. The proposed approach estimates the imperfection in the output of the prediction model from the imperfection in its inputs. It incorporates two steps:

  1. 1

    Computing membership functions for input variables for the model of land cover change prediction, and

  2. 2

    Propagating imperfections of input variables throughout this model and determining the effect of these imperfection in the model. A probabilistic collocation method is used to propagate imperfection.

Experimental results show the effectiveness of the proposed approach in improving both computation time and prediction of the land cover change of the Saint-Denis region, Reunion Island.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Babuska, I., Nobile, F., Tempone, R.: A stochastic collocation method for elliptic partial differential equations with random input data. SIAM Journal on Numerical Analysis 123(1), 1005–1034 (2007)

    Article  MathSciNet  Google Scholar 

  2. Boulila, W., Farah, I.R., Ettabaa, K.S., Solaiman, B., Ben Ghézala, H.: A data mining based approach to predict Spatio-temporal changes in satellite images. International Journal of Applied Earth 13(3), 386–395 (2011)

    Article  Google Scholar 

  3. Boulila, W., Farah, I.R., Ettabaa, K.S., Solaiman, B.: Combining decision fusion and uncertainty propagation to improve land cover change prediction insatellite image databases. Journal of Multimedia Processing and Technologies 2(3), 127–139 (2011)

    Google Scholar 

  4. Boulila, W., Farah, I.R.: S.Ettabaa K., Solaiman B., Ben Ghézala H.: Spatiotemporal modeling for knowledge discovery in satellite image databases. In: CORIA 2010: Conference en Recherche d’Information et Applications, Sousse, pp. 35–49 (2010)

    Google Scholar 

  5. Ghanem, R.G., Spanos, P.D.: Stochastic Finite Elements: A Spectral Approach. Springer (1991)

    Google Scholar 

  6. Hosder, S., Walters, R.W., Perez, R.: A non-intrusive polynomial chaos method for uncertainty propagation in CFD simulations. In: Proceedings of the 44th AIAA Aerospace Sciences Meeting, vol. 14, pp. 10649–10667 (2006)

    Google Scholar 

  7. Isukapalli, S.S.: Uncertainty Analysis of Transport-Transformation Models. PhD Thesis, The State University of New Jersey (1999)

    Google Scholar 

  8. Knopp, J.S., Blodgett, M.P., Cherry, M.R.: Probabilistic collocation method for NDE problems with uncertain parameters with arbitrary distributions, Air Force Research Laboratory Materials and Manufacturing Directorate,Wright-Patterson Air Force Base, Air Force Materiel Command, United States Air Force (2011)

    Google Scholar 

  9. Loeven, G.J.A., Witteveen, J.A.S., Bijlz, H.: Probabilistic Collocation: An Efficient Non- Intrusive Approach For Arbitrarily Distributed Parametric Uncertainties. In: 45th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevad, January 8-11 (2007)

    Google Scholar 

  10. Matteoli, S., Veracini, T., Diani, M., Corsini, G.: Models and Methods forAutomated Background Density in Hyperspectral Anomaly Detection. IEEE Transactions on Geosciences and Remote Sensing 51(5), 2837–2852 (2013)

    Article  Google Scholar 

  11. Tatang, M., Pan, W., Prinn, R., McRae, G.: An efficient method for parametric uncertainty analysis of numerical geophysical models. J. Geophys. 102(18), 21925–21932 (1997)

    Article  Google Scholar 

  12. Walter, L., Randeu, M., Kozu, T., Shimomai, T., Schönhuber, M., Hashiguchi, H.: Estimation of raindrop size distribution parameters by maximum likelihood and L-moment methods: Effect of discretization. Atmospheric Research 112, 1–11 (2012)

    Article  Google Scholar 

  13. Walters, R.W., Huyse, L.: Stochastic methods for fluid mechanics - an introduction.Technical Report in preparation, ICASE, NASA Langley Research Center, Hampton, VA (2001)

    Google Scholar 

  14. Webster, M., Tatang, M.A., McRae, G.J.: Application of the Probabilistic Collocation Method for an Uncertainty Analysis of a simple Ocean Model, MIT Joint Program on the Science and Policy of Global Change, Cambridge, Ma (1996)

    Google Scholar 

  15. Webster, M., Calbo, J., Pan, W., Prinn, R.G., McRae, G.J.: Parameterization of urban sub-grid scale processes in global atmospheric chemistry models (1997)

    Google Scholar 

  16. Wiener, N.: The homogeneous chaos. American Journal of Mathematics 60(13), 897–936 (1938)

    Article  MathSciNet  Google Scholar 

  17. Xiu, D., Karniadakis, G.E.: The Wiener-Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24(2), 619–644 (2002)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire RIADI, Ecole Nationale des Sciences de l’Informatique, Campus Universitaire de la Manouba, 2010, la Manouba, Tunisie

    Amine Bouatay, Wadii Boulila & Imed Riadh Farah

  2. TELECOM-Bretagne, LaboratoireITI Technopole Brest Iroise, CS, 83818, 29238, Brest Cedex, France

    Wadii Boulila & Imed Riadh Farah

Authors
  1. Amine Bouatay
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wadii Boulila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Imed Riadh Farah
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Computer Science Division, Department of Electrical Engineering and Computer Sciences, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Bouatay, A., Boulila, W., Farah, I.R. (2014). An Approach for Imperfection Propagation: Application to Land Cover Change Prediction. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8467. Springer, Cham. https://doi.org/10.1007/978-3-319-07173-2_54

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-07173-2_54

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07172-5

  • Online ISBN: 978-3-319-07173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation