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...

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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
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