Paper Info
Title
Towards an uncertainty reduction framework for land-cover change prediction using possibility theory.
Abstract
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.
Year
DOI
Venue
2017
10.1007/s40595-016-0088-7
Vietnam J. Computer Science
Keywords
Field
DocType
LCC prediction, Parameter uncertainty, Structural uncertainty, Possibility theory, Sensitivity analysis
Data mining,Propagation of uncertainty,Computer science,Sensitivity analysis,Possibility theory,Uncertainty analysis,Knowledge base,Predictive modelling,Land cover,Uncertainty reduction theory
Journal
Volume
Issue
ISSN
4
3
2196-8896
Citations 
PageRank 
References 
4
0.40
13
Authors
3
Name
Order
Citations
PageRank
Ahlem Ferchichi1132.66
Wadii Boulila2528.98
Imed Riadh Farah38626.16