An Intelligent Possibilistic Approach to Reduce the Effect of the Imperfection Propagation on Land Cover Change Prediction

Ferchichi, Ahlem; Boulila, ‪Wadii; Farah, Imed Riadh

doi:10.1007/978-3-319-24306-1_51

Cited by 4 publications

(5 citation statements)

References 7 publications

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“…The authors in [6] [7] [8] tend to converge to the conclusion that epistemic uncertainties (for example, lack of knowledge, incomplete information, or ambiguity) are different to aleatory uncertainties. Their main problem is that classical probability theory does not make a clear distinction between epistemic and aleatory uncertainties in the way they are represented, i.e., both of them are described with a probability distribution.…”

Section: Introduction and Reviewmentioning

confidence: 99%

Section: Introduction and Reviewmentioning

confidence: 99%

“…Nevertheless, some authors still argue that probability theory alone is enough to represent aleatory and epistemic uncertainty [6] [7] [8]. Moreover, they consider that the axioms of probability, including additivity, are a natural consequence of intuition, and a necessary condition to reason and make decision under uncertainty.…”

Section: Introduction and Reviewmentioning

confidence: 99%

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Propagating aleatory and epistemic uncertainty in land cover change prediction process

Ferchichi

Boulila

Farah

2017

Ecological Informatics

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An objective of satellite remote sensing is to predict or characterize the land cover change (LCC) over time. Sometimes we are capable of describing the changes of land cover with a probability distribution. However, we need sufficient knowledge about the natural variability of these changes, which is not always possible. In general, uncertainties can be subdivided into aleatory and epistemic. The main problem is that classical probability theory does not make a clear distinction between aleatory and epistemic uncertainties in the way they are represented, i.e., both of them are described with a probability distribution. The aim of this paper is to propagate the aleatory and epistemic uncertainty associated with both input parameters (features extracted from satellite image object) and model structure of LCC prediction process using belief function theory. This will help reducing in a significant way the uncertainty about future changes of land cover. In this study, the changes prediction of land cover in Cairo region, Egypt for next 16 years (2030) is anticipated using multi-temporal Landsat TM5 satellite images in 1987 and 2014. The LCC prediction model results indicated that 15% of the agriculture and 6.5% of the desert will be urbanized in 2030. We conclude that our method based on belief function theory has a potential to reduce uncertainty and improve the prediction accuracy and is applicable in LCC analysis.

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Section: Introduction and Reviewmentioning

confidence: 99%

Section: Introduction and Reviewmentioning

confidence: 99%

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Propagating aleatory and epistemic uncertainty in land cover change prediction process

Ferchichi

Boulila

Farah

2017

Ecological Informatics

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“…By no means do the promoters of theories pretend to replace probability theory; they just present different levels of expressiveness that leave room for properly representing the lack of background knowledge [4]. The most common theories that are used from these alternatives are imprecise probabilities [5], random sets [6], belief function theory [7], fuzzy sets [8], and possibility theory [9]. In our context of continuous measurements, the possibility theory is more adapted, because it generalises interval analysis and provides a bridge with probability theory by its ability to represent a family of probability distributions.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, model optimization with multiple uncertainty sources is complex and very time-consuming task. However, the sensitivity analysis has been proved to be efficient and robust to find the most important sources of uncertainty that have effect on LCC prediction models output [1,9,10]. Parameter sensitivity analysis allows to examine effects of model parameter on results, whereas structural sensitivity analysis allows to modify the structure of the model and to identify the possible structural factors that affect the robustness of the results (vary structure of model and see impact on results and tradeoffs between choices).…”

Section: Introductionmentioning

confidence: 99%

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

Ferchichi

Boulila

Farah

2016

Vietnam J Comput Sci

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This paper presents an approach for reducing uncertainty related to the process of land-cover change (LCC) prediction. LCC prediction models have, almost, two sources of uncertainty which are the uncertainty related to model parameters and the uncertainty related to model structure. These uncertainties have a big impact on decisions of the prediction model. To deal with these problems, the proposed approach is divided into three main steps: (1) an uncertainty propagation step based on possibility theory is used as a tool to evaluate the performance of the model; (2) a sensitivity analysis step based on Hartley-like measure is then used to find the most important sources of uncertainty; and (3) a knowledge base based on machine learning algorithm is built to identify the reduction factors of all uncertainty sources of parameters and to reshape their values to reduce in a significant way the uncertainty about future changes of land cover. In this study, the present and future growths of two case studies were anticipated using multi-temporal Spot-4 and Landsat satellite images. These data are used for the preparation of prediction map of year 2025. The results show that our approach based on possibility theory has a potential for reducing uncertainty in LCC prediction modeling. B Ahlem Ferchichi

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Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique

Wajid

Hussain

Huang

2018

Expert Systems with Applications

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An Intelligent Possibilistic Approach to Reduce the Effect of the Imperfection Propagation on Land Cover Change Prediction

Cited by 4 publications

References 7 publications

Propagating aleatory and epistemic uncertainty in land cover change prediction process

Propagating aleatory and epistemic uncertainty in land cover change prediction process

Towards an uncertainty reduction framework for land-cover change prediction using possibility theory

Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH): A Novel Feature Extraction Technique

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