Application of random forest and generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness

Environmental Modelling and Software

Volumn 97, Issue , 2017, Pages 112-129

  Li, Jin ^a   Alvarez, Belinda ^b,c   Siwabessy, Justy ^a   Tran, Maggie ^a   Huang, Zhi ^a   Przeslawski, Rachel ^a   Radke, Lynda ^a   Howard, Floyd ^a   Nichol, Scott ^a  

^a GEOSCIENCE AUSTRALIA   (Australia)

^b Museum and Art Gallery Northern Territory   (Australia)

^c LUND UNIVERSITY   (Sweden)

Author keywords

Feature selection; Machine learning; Model selection; Predictive accuracy; Spatial prediction; Spatial predictive model

Indexed keywords

DECISION TREES; ECOSYSTEMS; FEATURE EXTRACTION; FORECASTING; LEARNING SYSTEMS; RANDOM FORESTS; SPATIAL DISTRIBUTION;

GENERALISED LINEAR MODELS; GEOSTATISTICAL TECHNIQUES; MODEL SELECTION; NON-LINEAR RELATIONSHIPS; PREDICTIVE ACCURACY; PREDICTIVE MODELING; SPATIAL PREDICTION; VARIABLE SELECTION METHODS;

PREDICTIVE ANALYTICS;

ACCURACY ASSESSMENT; ECOSYSTEM MANAGEMENT; GENETIC MARKER; GEOSTATISTICS; MACHINE LEARNING; MARINE ECOSYSTEM; PREDICTION; RANDOM WALK METHOD; SPATIAL DISTRIBUTION; SPECIES RICHNESS; SPONGE;

EID: 85026776020     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2017.07.016     Document Type: Article

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