Conference proceeding
Underground water dam levels and energy consumption prediction using computational intelligence techniques
2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp 94-99
May 2014
Abstract
Three computational intelligence algorithms (k-nearest neighbors, a naïve Bayes' classifier, and decision trees) were applied on a double pump station mine to monitor and predict the dam levels and energy consumption. This work was carried out to inspect the feasibility of using computational intelligence in certain aspects of the mining industry. If successful, computational intelligence systems could lead to improved safety and reduced electrical energy consumption. The results show k nearest neighbors' technique to be more efficient when compared with decision trees, and naïve Bayes' classifier techniques in terms of predicting underground dam levels and pumps energy consumption.
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3 citations in Scopus
Details
- Title
- Underground water dam levels and energy consumption prediction using computational intelligence techniques
- Creators
- Ali N. Hasan - University of JohannesburgBhekisipho Twala - University of JohannesburgTshilidzi Marwala - University of Johannesburg
- Publication Details
- 2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), pp 94-99
- Publisher
- IEEE
- Number of pages
- 6
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Web of Science ID
- WOS:000364200500015
- Scopus ID
- 2-s2.0-84904547795
- Other Identifier
- 991022004778704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Information Systems