Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines

Ore Geology Reviews

Volumn 71, Issue , 2015, Pages 804-818

  Rodriguez Galiano, V ^a   Sanchez Castillo, M ^b   Chica Olmo, M ^c   Chica Rivas, M ^c  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b UNIVERSITY OF CAMBRIDGE   (United Kingdom)

^c UNIVERSITY OF GRANADA   (Spain)

Author keywords

Data driven modelling; Hyperion; Machine learning; Mineral potential; Mineral prospectivity mapping

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION TREES; FORESTRY; LEARNING ALGORITHMS; LEARNING SYSTEMS; MAPPING; MINERALS; NATURAL RESOURCES EXPLORATION; NEURAL NETWORKS; ORE DEPOSITS; REGRESSION ANALYSIS; SUPPORT VECTOR MACHINES;

DATA DRIVEN MODELLING; DATA-DRIVEN METHODS; HYPERION; MACHINE LEARNING ALGORITHM (MLAS); MINERAL PROSPECTIVITY MAPPINGS; STABILITY AND ROBUSTNESS; SUPPORT VECTOR MACHINE (SVMS); TRAINING PARAMETERS;

PARAMETER ESTIMATION;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; EPITHERMAL DEPOSIT; GOLD; HYPERION; MAPPING; MINERAL EXPLORATION; MINERALIZATION; ORE DEPOSIT;

ALMERIA [ANDALUCIA]; ANDALUCIA; RODALQUILAR; SPAIN;

EID: 84938951730     PISSN: 01691368     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.oregeorev.2015.01.001     Document Type: Article

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