Authors:
Ahlem Ferchichi
;
Wadii Boulila
and
Imed Riadh Farah
Affiliation:
Ecole Nationale des Sciences de l’Informatique, Tunisia
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 div
ided 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.
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