Conference proceeding
Predicting Mine Dam Levels and Energy Consumption Using Artificial Intelligence Methods
PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE FOR ENGINEERING SOLUTIONS (CIES), pp.171-175
01 Jan 2013
Abstract
Four machine learning algorithms (artificial neural networks, a naive Bayes' classifier, a support vector machines and decision trees) were applied for a single pump station mine to monitor and predict the dam levels and energy consumption. This work was undertaken to investigate the feasibility of using artificial intelligence in certain aspects of the mining industry. If successful, artificial intelligence systems could lead to improved safety and reduced electrical energy consumption. The results show neural networks to he more efficient when compared with support vector machines, a naive Bayes' classifier and in particular, decision trees in terms of predicting underground dam levels. Artificial neural networks showed 60% accuracy, out-performing support vector machine, naive Bayes' classifier and decision trees. For the prediction of water pump energy consumption, an artificial neural network and a naive Bayes' classifier had the same accuracy of 99.0%, whereas a support vector machine and decision trees achieved a lower accuracy.
Metrics
1 Record Views
Details
- Title
- Predicting Mine Dam Levels and Energy Consumption Using Artificial Intelligence Methods
- Creators
- Ali N. Hasan - University of JohannesburgBhekisipho Twala - University of JohannesburgTshilidzi Marwala - University of Johannesburg
- Publication Details
- PROCEEDINGS OF THE 2013 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE FOR ENGINEERING SOLUTIONS (CIES), pp.171-175
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Identifiers
- 991022004771104721