Cited by 1,576Geochemistry and Geologic MappingGeological Modeling and AnalysisMachine Learning in Materials Science

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

Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., Chica-Rivas, M.·2015·Ore Geology Reviews

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Crossref · 10.1016/j.oregeorev.2015.01.001

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109 of 109 references verified0 citation errors · 0 not checkable

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Authors
Rodriguez-Galiano, V., Sanchez-Castillo, M., Chica-Olmo, M., & Chica-Rivas, M.
Affiliations
  • University of Southampton (GB)
  • Universidad de Granada (ES)
Corresponding author
Rodriguez-Galiano, V.
Venue
Ore Geology Reviews
Publisher
Elsevier BV
Published
2015-12
Volume
71
Pages
804-818
ISSN
0169-1368 (ISSN-L)
OpenAlex
W1983865151
Cited by
1,576
References
109
Citations by year
2026: 207 · 2025: 307 · 2024: 285 · 2023: 221 · 2022: 154 · 2021: 144 · 2020: 92 · 2019: 82 · 2018: 42 · 2017: 25 · 2016: 9 · 2015: 7
FWCI
27.13
Type
journal article
Language
en
Open access
closed
Locations
  • Ore Geology Reviews (publishedVersion)
  • Helmholtz Centre for Ocean Research Kiel (GEOMAR) (acceptedVersion)
  • ePrints Soton (University of Southampton) (submittedVersion)
Funders
  • European Commission
  • Spanish MINECO (BIA2013-43462-P)
  • Junta de Andalucía (RNM122)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Geochemistry and Geologic Mapping
  • Physical Sciences › Earth and Planetary Sciences › Geochemistry and Petrology › Geological Modeling and Analysis
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
mineral prospectivity mapping, random forest, artificial neural networks, regression trees, support vector machine, epithermal gold deposits, algorithm comparison, hyperparameter sensitivity, training data size, model interpretability

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