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Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling / Carsten Krüger in International journal of geographical information science IJGIS, vol 29 n° 1 (January 2015)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 29 n° 1 (January 2015) . - pp 111 - 131 Titre : Bayesian belief networks as a versatile method for assessing uncertainty in land-change modeling Type de document : Article/Communication Auteurs : Carsten Krüger, Auteur ; Tobia Lakes, Auteur Année de publication : 2015 Article en page(s) : pp 111 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] aide à la décision [Termes IGN] Amazonie [Termes IGN] Brésil [Termes IGN] changement d'occupation du sol [Termes IGN] changement d'utilisation du sol [Termes IGN] déboisement [Termes IGN] incertitude des données [Termes IGN] modèle conceptuel de données spatio-temporelles [Termes IGN] réseau bayesien [Termes IGN] théorie de Dempster-Shafer Résumé : (auteur) Land-use and land-cover change modeling helps us to understand the driving factors and impacts of human-induced land changes better, and depict likely future development paths. Uncertainty associated with various steps in the modeling process substantially influences the reliability of the results, but until now it has only rarely been addressed. In this study, we explore uncertainty in land-change modeling using a probabilistic approach based on Bayesian belief networks. We apply this approach to a case study of deforestation in the Brazilian Amazon and identify three modeling steps as sources of uncertainty: model structure, variable selection, and data preprocessing. For these three steps, we quantify the uncertainty and the respective impact on the outcome accuracy. The results indicate remarkable uncertainties in each of the steps. We demonstrate that a higher uncertainty in the land-change modeling process does not necessarily lead to a lower accuracy of the modeling outcome. Moreover, we show that the different uncertainty sources only slightly influence the ratio between quantity disagreement and allocation disagreement for the modeling outcome. We conclude that uncertainty is inherent in land-change modeling, and that future studies should address this uncertainty more explicitly to improve the robustness of modeling outcomes for science and decision-making. Numéro de notice : A2015-532 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2014.949265 En ligne : http://www.tandfonline.com/doi/full/10.1080/13658816.2014.949265 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77763 [article]