Detecting Methane Outbreaks from Time Series Data with Deep Neural Networks

By Krzysztof Pawlowski, Karol Kurach. 2015

11 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Safety, Risk, Reliability and Quality, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Kasun Bandara
2. Christoph Bergmeir
3. Slawek Smyl

In Signal Processing

1. Christoph Bergmeir
2. Kasun Bandara
3. Slawek Smyl

Citations in the corpus

• Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, background): “…Natural Language Processing (Mikolov et al., 2010), machine translation (Sutskever et al., 2014), and speech recognition (Graves et al., 2013), and are also gaining popularity in time series research (Fei and Yeung, 2015; Paw lowski and Kurach, 2015; Lipton et al., 2015; Zimmermann et al., 2012).”