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Authors: Ahlem Ferchichi ; Wadii Boulila and Imed Riadh Farah

Affiliation: Ecole Nationale des Sciences de l’Informatique, Tunisia

ISBN: 978-989-758-157-1

Keyword(s): LCC Prediction, Imperfection Propagation, Parameter and Model Imperfection, Aleatory and Epistemic Imperfection, Correlated Parameters, Evidence Theory.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Fuzzy Image, Speech and Signal Processing, Vision and Multimedia ; Fuzzy Systems ; Soft Computing ; Soft Computing and Intelligent Agents

Abstract: The identification and the propagation of imperfection are important. In general, imperfection in land cover change (LCC) prediction process can be categorized as both aleatory and epistemic. This imperfection, which can be subdivided into parameter and structural model imperfection, is recognized to have an important impact on results. On the other hand, correlation of input system parameters is often neglected when modeling this system. However, correlation of parameters often blurs the model imperfection and makes it difficult to determine parameter imperfection. Several studies in literature depicts that evidence theory can be applied to model aleatory and epistemic imperfection and to solve multidimensional problems, with consideration of the correlation among parameters. The effective contribution of this paper is to propagate the imperfection associated with both correlated input parameters and LCC prediction model itself using the evidence theory. The proposed approach is divi ded into two main steps: 1) imperfection identification step is used to identify the types of imperfection (aleatory and/or epistemic), the sources of imperfections, and the correlations of the uncertain input parameters and the used LCC prediction model, and 2) imperfection propagation step is used to propagate aleatory and epistemic imperfection of correlated input parameters and model structure using the evidence theory. The results show the importance to propagate both parameter and model structure imperfection and to consider correlation among input parameters in LCC prediction model. In this study, the changes prediction of land cover in Saint-Denis City, Reunion Island of next 5 years (2016) was anticipated using multi-temporal Spot-4 satellite images in 2006 and 2011. Results show good performances of the proposed approach in improving prediction of the LCC of the Saint-Denis City on Reunion Island. (More)

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Paper citation in several formats:
Ferchichi, A.; Boulila, W. and Farah, I. (2015). Using Evidence Theory in Land Cover Change Prediction to Model Imperfection Propagation with Correlated Inputs Parameters.In Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (ECTA 2015) ISBN 978-989-758-157-1, pages 47-56. DOI: 10.5220/0005595800470056

@conference{fcta15,
author={Ahlem Ferchichi. and Wadii Boulila. and Imed Riadh Farah.},
title={Using Evidence Theory in Land Cover Change Prediction to Model Imperfection Propagation with Correlated Inputs Parameters},
booktitle={Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (ECTA 2015)},
year={2015},
pages={47-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005595800470056},
isbn={978-989-758-157-1},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Computational Intelligence - Volume 2: FCTA, (ECTA 2015)
TI - Using Evidence Theory in Land Cover Change Prediction to Model Imperfection Propagation with Correlated Inputs Parameters
SN - 978-989-758-157-1
AU - Ferchichi, A.
AU - Boulila, W.
AU - Farah, I.
PY - 2015
SP - 47
EP - 56
DO - 10.5220/0005595800470056

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