Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks

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 met...

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Bibliographic Details
Published inRough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp. 475 - 484
Main Authors Pawłowski, Krzysztof, Kurach, Karol
Format Book Chapter
LanguageEnglish
Published Cham Springer International Publishing 2015
SeriesLecture Notes in Computer Science
Subjects
Ensemble methods
Hazard monitoring systems
Machine learning
Recurrent neural networks
Time series forecasting
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ISBN331925782X
9783319257822
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-25783-9_42

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Summary: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.
Bibliography:K. Pawłowski and K. Kurach—Both authors contributed equally.
ISBN:331925782X
9783319257822
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-319-25783-9_42