Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

Data-Driven Predictive Modeling of Mineral Prospectivity Using Random Forests: A Case Study in Catanduanes Island (Philippines)

The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Natural resources research (New York, N.Y.) N.Y.), 2016-03, Vol.25 (1), p.35-50
Hauptverfasser: Carranza, Emmanuel John M., Laborte, Alice G.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Case studies
Chemistry and Earth Sciences
Computer Science
Copper
Earth and Environmental Science
Earth Sciences
Fossil Fuels (incl. Carbon Capture)
Geography
Gold
Machine learning
Mathematical Modeling and Industrial Mathematics
Mineral deposits
Mineral Resources
Modelling
Original Paper
Physics
Prediction models
Spatial analysis
Statistics for Engineering
Sustainable Development
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The Random Forests (RF) algorithm is a machine learning method that has recently been demonstrated as a viable technique for data-driven predictive modeling of mineral prospectivity, and thus, it is instructive to further examine its usefulness in this particular field. A case study was carried out using data from Catanduanes Island (Philippines) to investigate further (a) if RF modeling can be used for data-driven modeling of mineral prospectivity in areas with few (i.e.,
ISSN:1520-7439
1573-8981
DOI:10.1007/s11053-015-9268-x