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International Joint Conference on Computational Intelligence

IJCCI 2015: Computational Intelligence pp 270–290Cite as

Improvement of LCC Prediction Modeling Based on Correlated Parameters and Model Structure Uncertainty Propagation

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 669))

Abstract

Land cover change (LCC) mapping is one of the basic tasks for environmental monitoring and management. The most significant factors in determining the performance of model of LCC prediction are its structure and parameter optimization. However, these factors are generally marred by uncertainties which affect the reliability of decision about changes. The reduction of these uncertainties is deemed as essential elements for LCC prediction modeling. Propagation of uncertainty appears as good alternative for decreasing the uncertainty related to LCC prediction process and therefore obtain more relevant decision. On the other hand, correlation analysis between model parameters is often neglected. This affects the reliability of the model and makes it difficult to better determine the uncertainty related to model parameters. Several studies in literature depicts that evidence theory can be applied to propagate uncertainty associated to LCC prediction models and to solve multidimensional problems. This paper presents an effective optimization scheme for the LCC prediction modeling based on the uncertainty propagation of model parameters and model structure. Uncertainty propagation is analyzed by using evidence theory without and with considering correlations. In this study, change prediction of land cover in Saint-Denis City, Reunion Island of next 5 years (2016) was anticipated using multi-temporal Spot-4 satellite images acquired at the dates 2006 and 2011. Results show good performances of the proposed approach in improving prediction of the LCC. Results also demonstrate that the proposed approach is an effective and efficient method due to its adequate degree of accuracy.

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Authors and Affiliations

  1. Laboratoire RIADI, Ecole Nationale des Sciences de l’Informatique, Manouba, Tunisia

    Ahlem Ferchichi, Wadii Boulila & Imed Riadh Farah

Authors
  1. Ahlem Ferchichi
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  2. Wadii Boulila
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  3. Imed Riadh Farah
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Corresponding author

Correspondence to Ahlem Ferchichi .

Editor information

Editors and Affiliations

  1. Computer Architecture and Computer Technology, Universidad de Granada, Granada, Spain

    Juan Julián Merelo

  2. aSEEB-ISR-IST, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  3. Facultad de Informática, University of Murcia, Murcia, Spain

    José M. Cadenas

  4. Departamento de Engenharia Informatica, University of Coimbra, Coimbra, Portugal

    António Dourado Correia

  5. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  6. Campus de Gambelas, Universidade do Algarve, Faro, Portugal

    António Ruano

  7. Escola Superior de Tecnologia de Setúbal, Polytechnic Institute of Setúbal/INSTICC, Setúbal, Portugal

    Joaquim Filipe

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Ferchichi, A., Boulila, W., Farah, I.R. (2017). Improvement of LCC Prediction Modeling Based on Correlated Parameters and Model Structure Uncertainty Propagation. In: Merelo, J.J., et al. Computational Intelligence. IJCCI 2015. Studies in Computational Intelligence, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-319-48506-5_14

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  • DOI: https://doi.org/10.1007/978-3-319-48506-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48504-1

  • Online ISBN: 978-3-319-48506-5

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