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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

Early Warning System for Seismic Events in Coal Mines Using Machine Learning

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DOI: http://dx.doi.org/10.15439/2016F420

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 213220 ()

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Abstract. This document describes an approach to the problem of predicting dangerous seismic events in active coal mines up to 8 hours in advance. It was developed as a part of the AAIA‘16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. The solutions presented consist of ensembles of various predictive models trained on different sets of features. The best one achieved a winning score of 0.939 AUC.


  1. Wyższy Urząd Górniczy, “Wypadkowość w górnictwie od 1 stycznia 2015 do 31 grudnia 2015,” 2015, in Polish, last accessed 18 April 2016. [Online]. Available: http://www.wug.gov.pl/bhp/Statystyki_archiwalne_2015
  2. A. Zagorecki, Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing: 15th International Conference, RSFDGrC 2015, Tianjin, China, November 20-23, 2015, Proceedings. Cham: Springer International Publishing, 2015, ch. Prediction of Methane Outbreaks in Coal Mines from Multivariate Time Series Using Random Forest, pp. 494–500. [Online]. Available: http://dx.doi.org/10.1007/978-3-319-25783-9_44
  3. A. Janusz, M. Sikora, Ł. Wróbel, and D. Ślęzak, “Predicting Dangerous Seismic Events: AAIA16 Data Mining Challenge,” in Proceedings of FedCSIS 2016. IEEE, 2016, in print September 2016.
  4. M. Sikora, “Induction and pruning of classification rules for prediction of microseismic hazards in coal mines,” Expert Systems with Applications, vol. 38, no. 6, pp. 6748–6758, 2011.
  5. Knowledge Pit, a host platform for data challenges, 2016, last accessed 18 April 2016. [Online]. Available: https://knowledgepit.fedcsis.org/
  6. T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” arXiv preprint https://arxiv.org/abs/1603.02754, 2016.
  7. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay, “Scikit-learn: Machine learning in Python,” Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011.
  8. L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, and G. Varoquaux, “API design for machine learning software: experiences from the scikit-learn project,” in ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 2013, pp. 108–122.
  9. T. K. Ho, “The random subspace method for constructing decision forests,” Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 20, no. 8, pp. 832–844, 1998.