@inproceedings{2294100fb3ea4dd7beada5a173f53866,
title = "Underground water dam levels and energy consumption prediction using computational intelligence techniques",
abstract = "Three computational intelligence algorithms (k-nearest neighbors, a na{\"i}ve 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 na{\"i}ve Bayes' classifier techniques in terms of predicting underground dam levels and pumps energy consumption.",
keywords = "decision trees, energy monitoring, gold mines, k nearest neighbors, na{\"i}ve Bayes, prediction, underground pump stations, water pumping system",
author = "Hasan, {Ali N.} and Bhekisipho Twala and Tshilidzi Marwala",
year = "2014",
doi = "10.1109/CIVEMSA.2014.6841445",
language = "English",
isbn = "9781479926138",
series = "CIVEMSA 2014 - 2014 IEEE Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings",
publisher = "IEEE Computer Society",
pages = "94--99",
booktitle = "CIVEMSA 2014 - 2014 IEEE Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings",
address = "United States",
note = "2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2014 ; Conference date: 05-05-2014 Through 07-05-2014",
}