Determining factors that influence the dispersal of a pelagic species: A comparison between artificial neural networks and evolutionary algorithms

Ecological Modelling

Volumn 222, Issue 10, 2011, Pages 1657-1665

  Pontin, D R ^a   Schliebs, S ^b   Worner, S P ^a   Watts, M J ^c  

^a LINCOLN UNIVERSITY   (New Zealand)

^b AUCKLAND UNIVERSITY OF TECHNOLOGY   (New Zealand)

^c UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

Artificial neural networks; Ecological informatics; Evolutionary algorithms; Feature selection; Physalia

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; BAYESIAN CLASSIFIER; CNIDARIA; COASTAL REGIONS; COASTAL WATERS; DYNAMIC SYSTEMS; EAST COAST; ERADICATION PROGRAMS; FEATURE SELECTION; INDEPENDENT SYSTEMS; INFORMATICS; INVASIVE SPECIES; MODELLING METHOD; MOLECULAR DATA; MULTI LAYER PERCEPTRON; NEW ZEALAND; OCEANOGRAPHIC DATA; PELAGIC SPECIES; PHYSALIA; SUMMER SEASON; WAVE HEIGHTS; WIND DIRECTIONS;

BAYESIAN NETWORKS; COASTAL ZONES; ECOLOGY; EVOLUTIONARY ALGORITHMS; FEATURE EXTRACTION; INFORMATION SCIENCE; SODIUM;

NEURAL NETWORKS;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOMONITORING; COASTAL WATER; COASTAL ZONE; DISPERSAL; ENDANGERED SPECIES; ENVIRONMENTAL FACTOR; IDENTIFICATION METHOD; INVERTEBRATE; MARINE ENVIRONMENT; MOLECULAR ANALYSIS; NUMERICAL MODEL; OCEANOGRAPHY; PELAGIC ENVIRONMENT; SELECTION;

NEW ZEALAND; PACIFIC OCEAN; TASMAN SEA;

CNIDARIA; PHYSALIA; SIPHONOPHORA (HYDROZOANS);

EID: 79954615468     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2011.03.002     Document Type: Article

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