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(Article of periodic en Anglais - 2008)

Document title

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

Authors(s) and Affiliation(s)

MARMION M. (1 2) ; HJORT J. (3) ; THUILLER W. (4) ; LUOTO M. (1 2) ;
(1) Thule Inst., Univ., Oulu, FINLANDE
(2) Dep. of Geography, Univ., Oulu, FINLANDE
(3) Dep. of Geography, Univ., Helsinki, FINLANDE
(4) Lab. Ecologie Alpine, UMR CNRS 5553, Univ. Joseph Fourier, Grenoble, FRANCE

Abstract

This study compares the predictive accuracy of 8 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 (GLM), Generalized Additive Models (GAM), Multivariate Adaptive Regression Splines (MARS), Classification Tree Analysis (CTA) and Mixture Discriminant Analysis (MDA). The spatial distributions of 12 periglacial landforms types were recorded in sub-Arctic landscape of northern Finland in 2032 grid squares at a resolution of 25 ha. First, 3 topographic variables were implemented into the 8 modelling techniques (simple model), and then 6 other variables were added (3 soil and 3 vegetation variables; complex model) to reflect the environmental conditions of each grid square. The predictive accuracy was measured by 2 methods : the area under the curve (AUC) of a receiver operating characteristic (ROC) plot, and the Kappa index (k), based on spatially independent model evaluation data. The results encourage further applications of the novel modelling methods in geomorphology

Source

Article of periodic

published at : Earth surface processes and landforms / ISSN 0197-9337 / CODEN ESPLDB

Editor : Wiley, Chichester - ROYAUME-UNI (1981)

Millesime : 2008, vol. 33, no14 [pp. 2241-2254]

Bibliographic references : 2 p.

Collation : 4 fig., 5 tabl.

Language

Anglais

INIST-CNRS, Cote INIST : 17355

Digital Object Identifier

Go to electronic document thanks to its DOI : doi:10.1002/esp.1695

Prodig - Bibliographie Géographique Internationale (BGI)
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