Moving towards accurate monitoring and prediction of gold mine underground dam levels

Ali N. Hasan, Bhekisipho Twala, Tshilidzi Marwala

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

10 Citations (Scopus)

Abstract

In this paper a comparison between an ensembles (multi-classifier) constructed of several machine learning methods (support vector machine, artificial neural network, naive Bayesian classifier, decision trees, radial basis function and k nearest neighbors) versus each single classifiers of these methods in term of gold mine underground dam levels prediction is presented. The ensembles as well as the single classifiers are used to classify, thus monitoring and predicting the underground water dam levels on a single-pump station deep gold in South Africa. In order to improve the classification accuracy an ensemble was constructed based on each single classifier performance, therefore, five ensembles were built and tested. In terms of misclassification error, the results show the ensemble to be more efficient for classification of underground water dam levels compared to each of the single classifiers.

Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2844-2849
Number of pages6
ISBN (Electronic)9781479914845
DOIs
Publication statusPublished - 3 Sept 2014
Event2014 International Joint Conference on Neural Networks, IJCNN 2014 - Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Conference

Conference2014 International Joint Conference on Neural Networks, IJCNN 2014
Country/TerritoryChina
CityBeijing
Period6/07/1411/07/14

Keywords

  • Support vector machines
  • classification
  • de-watering system
  • ensembles
  • gold mines
  • naive Bayesian
  • neural networks
  • underground dam levels

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Moving towards accurate monitoring and prediction of gold mine underground dam levels'. Together they form a unique fingerprint.

Cite this