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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Application of RapidMiner and R Environments to Dangerous Seismic Events Prediction

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DOI: http://dx.doi.org/10.15439/2016F188

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 249252 ()

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Abstract. Underground coal mining is a branch of an industry which safety of operation is very dependent on the natural hazards. A proper seismic event prediction is a significant aspect of building classification models from the real data, which can affect the coal mining safety increase. In this paper four models, built in a well known data mining environments, are presented. The obtained models, depending on a given implementation of popular methods, occurred comparable to the best results from the competition.


  1. M. Sikora and B. Sikora, “Improving prediction models applied in systems monitoring natural hazards and ma- chinery,” International Journal of Applied Mathematics and Computer Science, vol. 22, no. 2, pp. 477–491, 2012. http://dx.doi.org/10.2478/v10006-012-0036-3. [Online]. Available: http://dx.doi.org/10.2478/v10006-012-0036-3
  2. M. Sikora and B. Sikora, “Rough natural hazards monitoring,” in Rough Sets: Selected Methods and Applications in Management and Engineering. Springer, 2012, pp. 163–179. [Online]. Available: http://dx.doi.org/10.1007/ 978-1-4471-2760-4-10
  3. A. Zagorecki, “Application of sensor fusion and data mining for prediction of methane concentration in coal mines,” Mining — Informatics, Automation and Electrical Engineering, vol. 524, no. 4, pp. 33–38, 2015.
  4. J. Kabiesz, B. Sikora, M. Sikora, and Ł. Wróbel, “Application of rule-based models for seismic hazard prediction in coal mines,” Acta Montanistica Slovaca, vol. 18, no. 3, 2013.
  5. J. Kabiesz, “Effect of the form of data on the quality of mine tremors hazard forecasting using neural networks,” Geotechnical & Geological Engineering, vol. 24, no. 5, pp. 1131–1147, 2006. http://dx.doi.org/10.1007/s10706-005-1136-8. [Online]. Available: http://dx.doi.org/10.1007/s10706-005-1136-8
  6. A. Leśniak and Z. Isakow, “Space-time clustering of seismic events and hazard assessment in the Zabrze-Bielszowice coal mine, Poland,” International Journal of Rock Mechanics and Mining Sciences, vol. 46, no. 5, pp. 918–928, 2009. http://dx.doi.org/10.1016/j.ijrmms.2008.12.003. [Online]. Available: http://dx.doi.org/10.1016/j.ijrmms.2008.12.003
  7. A. Janusz and et al., “Predicting dangerous seismic events in active coal mines: Summary of AAIA’16 data mining competition at knowledge pit,” Proc of FedCSIS 2016, vol. 00, no. 00, pp. 00–00, 2016.
  8. AAIA’16 data mining challenge: Predicting dangerous seismic events in active coal mines. [Online]. Available: https://knowledgepit.fedcsis.org/contest/view.php?id=112
  9. R Core Team, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2014. [Online]. Available: http://www.R-project.org
  10. RapidMiner. Rapidminer. [Online]. Available: http://rapidminer.com
  11. H2O platform. [Online]. Available: http://www.h2o.ai
  12. The definitive performance tuning guide for h2o deep learning. [Online]. Available: http://blog.h2o.ai/2015/02/deep-learning-performance/
  13. The caret package. [Online]. Available: http://topepo.github.io/caret/index.html
  14. Kibana software. [Online]. Available: http://www.elastic.co/products/kibana