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Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

Hazard monitoring systems play a key role in ensuring people’s safety. The problem of detecting dangerous levels of methane concentration in a coal mine was a subject of IJCRS’15 Data Challenge competition. The challenge was to predict, from multivariate time series data collected by sensors, if methane concentration reaches a dangerous level in the near future. In this paper we present our solution to this problem based on the ensemble of Deep Neural Networks. In particular, we focus on Recurrent Neural Networks with Long Short-Term Memory (LSTM) cells.

K. Pawłowski and K. Kurach—Both authors contributed equally.

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Authors and Affiliations

  1. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Krzysztof Pawłowski & Karol Kurach

Authors
  1. Krzysztof Pawłowski
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  2. Karol Kurach
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Corresponding author

Correspondence to Krzysztof Pawłowski .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Pawłowski, K., Kurach, K. (2015). Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_42

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

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