{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T10:36:12Z","timestamp":1768732572589,"version":"3.49.0"},"reference-count":22,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T00:00:00Z","timestamp":1554163200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2019,4,2]]},"abstract":"<jats:p>Seismic events prediction is a crucial task for preventing coal mine rock burst hazards. Currently, this task attracts increasing research enthusiasms from many mining experts. Considering the temporal characteristics of monitoring data, seismic events prediction can be abstracted as a time series prediction task. This paper contributes to address the problem of long-term historical dependence on seismic time series prediction with deep temporal convolution neural networks (CNN). We propose a dilated causal temporal convolution network (DCTCNN) and a CNN long short-term memory hybrid model (CNN-LSTM) to forecast seismic events. In particular, DCTCNN is designed with dilated CNN kernels, causal strategy, and residual connections; CNN-LSTM is established in a hybrid modeling way by utilizing advantage of CNN and LSTM. Based on these manners, both of DCTCNN and CNN-LSTM can extract long-term historical features from the monitoring seismic data. The proposed models are experimentally tested on two real-life coal mine seismic datasets. Furthermore, they are also compared with one traditional time series prediction method, two classic machine learning algorithms, and two standard deep learning networks. Results show that DCTCNN and CNN-LSTM are superior than the other five algorithms, and they successfully complete the seismic prediction task.<\/jats:p>","DOI":"10.1155\/2019\/7343784","type":"journal-article","created":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T23:11:12Z","timestamp":1554246672000},"page":"1-14","source":"Crossref","is-referenced-by-count":22,"title":["Seismic Events Prediction Using Deep Temporal Convolution Networks"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3297-4454","authenticated-orcid":true,"given":"Yue","family":"Geng","sequence":"first","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 10083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Su","sequence":"additional","affiliation":[{"name":"Guanghe Xinzhi (Beijing) Technology Co. Ltd, Beijing 100015, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunhong","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 10083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4678-5651","authenticated-orcid":true,"given":"Ce","family":"Han","sequence":"additional","affiliation":[{"name":"School of Mechanical Electronic & Information Engineering, China University of Mining and Technology, Beijing 10083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.13225\/j.cnki.jccs.2015.0080"},{"key":"2","first-page":"1161","volume":"38","year":"2013","journal-title":"Journal of the China Coal Society"},{"key":"3","first-page":"199","volume":"45","year":"2017","journal-title":"Coal Science and Technology"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1142\/S0219622006002258"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1016\/j.ssci.2011.08.065"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2016.03.015"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.4028\/www.scientific.net\/AMM.628.383"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmst.2013.08.014"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1007\/s11069-015-1842-3"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CP.1943-5487.0000553"},{"key":"12","volume-title":"Gradient flow in recurrent nets: the difficulty of learning long term dependencies","year":"2001"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.11.374"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.3390\/s17020273"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.tust.2018.08.029"},{"key":"22","first-page":"1624","volume":"37","year":"2012","journal-title":"Journal of China Coal Society"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2494-2"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0191939"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.12.022"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.3390\/s17020273"},{"key":"34","first-page":"91","volume":"55","year":"2010","journal-title":"Archives of Ming Sciences"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2019\/7343784.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2019\/7343784.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2019\/7343784.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,2]],"date-time":"2019-04-02T23:11:15Z","timestamp":1554246675000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2019\/7343784\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,2]]},"references-count":22,"alternative-id":["7343784","7343784"],"URL":"https:\/\/doi.org\/10.1155\/2019\/7343784","relation":{},"ISSN":["2090-0147","2090-0155"],"issn-type":[{"value":"2090-0147","type":"print"},{"value":"2090-0155","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,2]]}}}