Statistical consensus methods for improving predictive geomorphology maps

• 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, Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Laboratoire d'Ecologie Alpine (LECA), Université Joseph Fourier - Grenoble 1 (UJF)-Université Savoie Mont Blanc (USMB Université de Savoie Université de Chambéry )-Centre National de la Recherche Scientifique (CNRS)
• 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.science/halsde-00377980; https://doi.org/10.1016/j.cageo.2008.02.024

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.