Detecting methane outbreaks from time series data with deep neural networks

Krzysztof Pawłowski, Karol Kurach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationRough Sets, Fuzzy Sets, Data Mining and Granular Computing - 15th International Conference, RSFDGrC 2015, Proceedings
EditorsYiyu Yao, Jerzy W. Grzymala-Busse, Hong Yu, Qinghua Hu
PublisherSpringer Verlag
Pages475-484
Number of pages10
ISBN (Print)9783319257822
DOIs
StatePublished - 2015
Externally publishedYes
Event15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2015 - Tianjin, China
Duration: Nov 20 2015Nov 23 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9437
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2015
CountryChina
CityTianjin
Period11/20/1511/23/15

Keywords

  • Ensemble methods
  • Hazard monitoring systems
  • Machine learning
  • Recurrent neural networks
  • Time series forecasting

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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