Artificial neural networks: A new method for mineral prospectivity mapping

By Warick Brown, Tom Gedeon, D. Groves, R. Barnes. 2000

321 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/0b8455292b1729eec5e62cd7724c0a56d21cabaa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Victor Francisco Rodriguez-Galiano
2. Mario Chica-Olmo
3. Manuel Sanchez-Castillo
4. Mario Chica-Rivas

In Environmental Engineering

1. D. Abaye
2. Victor Francisco Rodriguez-Galiano
3. Mario Chica-Olmo
4. Joseph Otoo.
5. Ernest Yeboah Boateng

Citations in the corpus

• Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, unknown): “…Carranza and Hale, 2001; Harris et al., 2001, 2006; Mejía-Herrera et al., 2014) and (ii) artificial neural networks (Singer and Kouda, 1996, 1999; Brown et al., 2000, 2003a,b; Harris et al., 2003; Porwal et al., 2003, 2004; Rigol-Sanchez et al., 2003; Skabar, 2005; Behnia, 2007; Nykänen, 2008;…”
• Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines (2015, background): “, 1984), artificial neural networks (ANNs) (Brown et al., 2000; Porwal et al., 2003; Rigol-Sanchez et al., 2003; Rumelhart et al., 1986), support vector machines (SVMs) (Abedi et al.”
• Basic Tenets of Classification Algorithms K-Nearest-Neighbor, Support Vector Machine, Random Forest and Neural Network: A Review (2020, methodology): “Larger input datasets will lengthen classification times for NN more than for SVM and RF [4].”