Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines
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Random forest regression has the advantage of being able to handle high-dimensional data by using decision trees to classify variables.
“RFR has the advantage of being able to handle highdimensional data (Rodriguez-Galiano et al., 2015), by using decision trees to classify variables.”
From Global warming potential of lithium-ion battery cell production: Determining influential primary and secondary raw material supply routes (2023) · cites this paper as Rodriguez-Galiano et al., 2015
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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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