Evaluation of modelling techniques for forest site productivity prediction in contrasting ecoregions using stochastic multicriteria acceptability analysis (SMAA)

Environmental Modelling and Software

Volumn 26, Issue 7, 2011, Pages 929-937

  Aertsen, Wim ^a   Kint, Vincent ^a   Van Orshoven, Jos ^a   Muys, Bart ^a  

^a UNIVERSITY OF LEUVEN   (Belgium)

Author keywords

Artificial neural networks; Boosted regression trees; Generalized additive models; Mediterranean mountain forest; Predictive modelling; Site index; Temperate lowland forest

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BOOSTED REGRESSION TREES; GENERALIZED ADDITIVE MODEL; MEDITERRANEAN MOUNTAIN FOREST; PREDICTIVE MODELLING; SITE INDEX; TEMPERATE LOWLAND FOREST;

FORESTRY; LANDFORMS; LINEAR REGRESSION; MODELS; NEURAL NETWORKS; PRODUCTIVITY; SITE SELECTION; SOILS;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; ECOREGION; FORESTRY MODELING; MEDITERRANEAN ENVIRONMENT; MONTANE FOREST; RESOURCE MANAGEMENT; SITE INDEX; STOCHASTICITY; SUSTAINABLE FORESTRY; TEMPERATE FOREST;

FORECASTS; FORESTS; MATHEMATICAL MODELS; NEURAL NETWORKS; PRODUCTIVITY; RANDOM PROCESSES; REGRESSION ANALYSIS; SITE INDEX; SITES; SOIL;

BELGIUM; FLANDERS; TURKEY;

EID: 79953120983     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2011.01.003     Document Type: Article

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