Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

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

Predicting Dangerous Seismic Activity with Recurrent Neural Networks

,

DOI: http://dx.doi.org/10.15439/2016F134

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 239–243 ()

Full text

Abstract. In this paper we present a solution to the AAIA'16 Data Mining Challenge. The goal of the challenge was to predict, from multivariate time series data, periods of increased seismic activity which may cause life-threatening accidents in underground coal mines. Our solution is based on Recurrent Neural Network with Long Short-Term Memory cells. It requires almost no feature engineering, which makes it easily applicable to other domains with multivariate time series data. The method achieved the 5th place in the AAIA'16 competition, out of 203 teams.

References

  1. Marc Boullé. Prediction of methane outbreak in coal mines from historical sensor data under distribution drift. In Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pages 439–451. Springer, 2015.
  2. Ronan Collobert, Koray Kavukcuoglu, and Clément Farabet. Torch7: A matlab-like environment for machine learning. In BigLearn, NIPS Workshop, number EPFL-CONF-192376, 2011.
  3. C Allin Cornell. Engineering seismic risk analysis. Bulletin of the Seismological Society of America, 58(5):1583–1606, 1968.
  4. Long-jun Dong, Xi-bing Li, and PENG Kang. Prediction of rockburst classification using random forest. Transactions of Nonferrous Metals Society of China, 23(2):472–477, 2013.
  5. Longjun Dong, Xibing Li, and Gongnan Xie. Nonlinear methodologies for identifying seismic event and nuclear explosion using random forest, support vector machine, and naive bayes classification. In Abstract and Applied Analysis, volume 2014. Hindawi Publishing Corporation, 2014.
  6. Alan Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. Speech recognition with deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on, pages 6645–6649. IEEE, 2013.
  7. Alex Graves, Marcus Liwicki, Santiago Fernández, Roman Bertolami, Horst Bunke, and Jürgen Schmidhuber. A novel connectionist system for unconstrained handwriting recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(5):855–868, 2009.
  8. Marek Grzegorowski and Sebastian Stawicki. Window-based feature engineering for prediction of methane threats in coal mines. In Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pages 452–463. Springer, 2015.
  9. Barbara Hammer. On the approximation capability of recurrent neural networks. Neurocomputing, 31(1):107–123, 2000.
  10. David R Hanson, Thomas L Vandergrift, Matthew J DeMarco, and Kanaan Hanna. Advanced techniques in site characterization and mining hazard detection for the underground coal industry. International journal of coal geology, 50(1):275–301, 2002.
  11. Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.
  12. Andrzej Janusz, Marek Sikora, Łukasz Wróbel, and Dominik Ślęzak. Predicting Dangerous Seismic Events: AAIA16 Data Mining Challenge. In Proceedings of FedCSIS 2016. IEEE, 2016. In print September 2016.
  13. Andrzej Janusz, Marek Sikora, Łukasz Wróbel, Sebastian Stawicki, Marek Grzegorowski, Piotr Wojtas, and Dominik Śl ̨ezak. Mining data from coal mines: Ijcrs2̆01915 data challenge. In Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pages 429–438. Springer, 2015.
  14. Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint https://arxiv.org/abs/1412.6980, 2014.
  15. Petre Lameski, Eftim Zdravevski, Riste Mingov, and Andrea Kulakov. Svm parameter tuning with grid search and its impact on reduction of model over-fitting. In Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pages 464–474. Springer, 2015.
  16. A McGarr. Some applications of seismic source mechanism studies to assessing underground hazard. In Rockbursts and Seismicity in Mines., pages 199–208, 1984.
  17. Vinod Nair and Geoffrey E Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 807–814, 2010.
  18. Martin Sundermeyer, Ralf Schlüter, and Hermann Ney. Lstm neural networks for language modeling. In INTERSPEECH, 2012.
  19. Ilya Sutskever, Oriol Vinyals, and Quoc VV Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112, 2014.
  20. Paul J Werbos. Generalization of backpropagation with application to a recurrent gas market model. Neural Networks, 1(4):339–356, 1988.
  21. Adam Zagorecki. Prediction of methane outbreaks in coal mines from multivariate time series using random forest. In Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, pages 494–500. Springer, 2015.
  22. Adam Zagorecki. A versatile approach to classification of multivariate time series data. In 2015 Federated Conference on Computer Science and Information Systems, FedCSIS 2015, Lódz, Poland, September 13-16, 2015, pages 407–410, 2015.