Statistical consensus methods for improving predictive geomorphology maps

International audience A variety of predictive models is currently used to map the spatial distribution of earth surface processes and landforms. In this study, we tested statistical consensus methods in order to improve the predictive accuracy of geomorphological models. The distributions of 12 geo...

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Bibliographic Details
Published in:Computers & Geosciences
Main Authors: Marmion, M., Hjort, J., Thuiller, W., Luoto, M.
Other Authors: Thule Institute, University of Oulu, Department of Geography Oulu, Department of Geosciences and Geography Helsinki, Falculty of Science Helsinki, University of Helsinki-University of Helsinki, Laboratoire d'Ecologie Alpine (LECA), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2009
Subjects:
AUC
Uncertainty
Landforms
Predictive modelling
Adaptive regression splines
climate-change
species distributions
terrain parameters
finnish lapland
models
gis
classification
permafrost
europe
Arctic
Climate change
Northern Finland
Lapland
Online Access:https://hal.archives-ouvertes.fr/halsde-00377980
https://doi.org/10.1016/j.cageo.2008.02.024
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Summary:International audience A variety of predictive models is currently used to map the spatial distribution of earth surface processes and landforms. In this study, we tested statistical consensus methods in order to improve the predictive accuracy of geomorphological models. The distributions of 12 geomorphological formations were recorded at a resolution of 25 ha in a sub-arctic landscape in northern Finland. Nine environmental variables were used to predict probabilities of occurrence of the formations using eight state-of-the-art modelling techniques. The probability values of the models were combined using four different consensus methods. The accuracy of the models was calculated using spatially independent test data by the area under the curve (AUC) of a receiver-operating characteristic (ROC) plot. The mean AUC values of the geomorphological models varied between 0.711 and 0.755 based on single-model techniques, whereas the corresponding values based on consensus methods ranged from 0.752 to 0.782. The weighted average consensus method had the highest predictive performance of all methods. It improved the accuracy of 11 predictions out of 12. The results of this study suggest that the consensus methods have clear advantages over single-model predictions. The simplicity of the consensus methods makes it straightforward to implement them in predictive modelling studies in geomorphology. (C) 2008 Elsevier Ltd. All rights reserved.

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