A comparison of predictive methods in modelling the distribution of periglacial landforms in Finnish Lapland

• 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 2008
• Subjects: spatial modelling; statistical techniques; AUC; Kappa; predictive; performance; Adaptive regression splines; artificial neural-networks; sub-arctic; finland; species distributions; spatial autocorrelation; logistic-regression; climate-change; gis; permafrost; scale; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; [SDE.MCG]Environmental Sciences/Global Changes; [SDV.BID]Life Sciences [q-bio]/Biodiversity; [SDV.EE]Life Sciences [q-bio]/Ecology; environment; Arctic; Gam; Climate change; Northern Finland; Lapland
• Online Access: https://hal.archives-ouvertes.fr/halsde-00377979

International audience This study compares the predictive accuracy of eight state-of-the-art modelling techniques for 12 landforms types in a cold environment. The methods used are Random Forest (RF), Artificial Neural Networks (ANN), Generalized Boosting Methods (GBM), Generalized Linear Models (GL...