New hybrid predictive modeling principles for ammonium adsorption: The combination of Response Surface Methodology with feed-forward and Elman-Recurrent Neural Networks

2021 ◽  
pp. 127688
Author(s):  
Ozge Cagcag Yolcu ◽  
Fulya Aydın Temel ◽  
Ayşe Kuleyin
Keyword(s):  
Neural Networks ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Predictive Modeling ◽  
Recurrent Neural Networks ◽  
Feed Forward ◽  
Ammonium Adsorption ◽  
Elman Recurrent Neural Networks ◽  
Modeling Principles

An Analysis of the Use of Predictive Modeling with Business Intelligence Systems for Exploration of Precious Metals Using Biogeochemical Data

2013 ◽  
Vol 4 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Thomas A. Woolman ◽  
John C. Yi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Anomaly Detection ◽  
Predictive Modeling ◽  
Business Intelligence ◽  
Precious Metals ◽  
Geological Exploration ◽  
Feed Forward ◽  
Business Intelligence Systems ◽  
Modeling Techniques

This study addresses the use of predictive modeling techniques; primarily feed-forward artificial neural networks as a tool for forecasting geological exploration targets for gold prospecting. It also provides evidence of effectiveness of using Business Intelligence systems to model pathfinder variables, anomaly detection, and forecasting to locate potential exploration sites for precious metals. The results indicate that the use of advanced Business Intelligence systems can be of extremely high value to the extractive minerals exploration industry.

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