Underground water dam levels and energy consumption prediction using computational intelligence techniques

Ali N. Hasan, Bhekisipho Twala, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationCIVEMSA 2014 - 2014 IEEE Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings
PublisherIEEE Computer Society
Pages94-99
Number of pages6
ISBN (Print)9781479926138
DOIs
Publication statusPublished - 2014
Event2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2014 - Ottawa, ON, Canada
Duration: 5 May 20147 May 2014

Publication series

NameCIVEMSA 2014 - 2014 IEEE Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, Proceedings

Conference

Conference2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2014
Country/TerritoryCanada
CityOttawa, ON
Period5/05/147/05/14

Keywords

  • decision trees
  • energy monitoring
  • gold mines
  • k nearest neighbors
  • naïve Bayes
  • prediction
  • underground pump stations
  • water pumping system

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications

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