Predicting dangerous seismic activity with Recurrent Neural Networks

Karol Kurach, Krzysztof Pawlowski

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

6 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016
EditorsMaria Ganzha, Marcin Paprzycki, Leszek Maciaszek
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages239-243
Number of pages5
ISBN (Electronic)9788360810903
DOIs
StatePublished - Nov 3 2016
Externally publishedYes
Event2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016 - Gdansk, Poland
Duration: Sep 11 2016Sep 14 2016

Publication series

NameProceedings of the 2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016

Conference

Conference2016 Federated Conference on Computer Science and Information Systems, FedCSIS 2016
CountryPoland
CityGdansk
Period9/11/169/14/16

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems
  • Computer Science (miscellaneous)

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