Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Machine learning methods for earthquake prediction: a Survey : A survey. / Galkina, Alyona; Grafeeva, Natalia.
Proceedings of the FourthConference on Software Engineering and Information Management (SEIM 2019). ред. / Y. Litvinov; P. Trifonov. RWTH Aahen University, 2019. стр. 25-32 (CEUR Workshop Proceedings; Том 2372).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Machine learning methods for earthquake prediction: a Survey
T2 - 4th Conference on Software Engineering and Information Management, SEIM 2019
AU - Galkina, Alyona
AU - Grafeeva, Natalia
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Earthquakes are one of the most dangerous natural disasters, primarily due to the fact that they often occur without an explicit warning, leaving no time to react. This fact makes the problem of earthquake prediction extremely important for the safety of humankind. Despite the continuing interest in this topic from the scientific community, there is no consensus as to whether it is possible to find the solution with sufficient accuracy. However, successful application of machine learning techniques to different fields of research indicates that it would be possible to use them to make more accurate short-term forecasts. This paper reviews recent publications where application of various machine learning based approaches to earthquake prediction was studied. The aim is to systematize the methods used and analyze the main trends in making predictions. We believe that this research will be useful and encouraging for both earthquake scientists and beginner researchers in this field.
AB - Earthquakes are one of the most dangerous natural disasters, primarily due to the fact that they often occur without an explicit warning, leaving no time to react. This fact makes the problem of earthquake prediction extremely important for the safety of humankind. Despite the continuing interest in this topic from the scientific community, there is no consensus as to whether it is possible to find the solution with sufficient accuracy. However, successful application of machine learning techniques to different fields of research indicates that it would be possible to use them to make more accurate short-term forecasts. This paper reviews recent publications where application of various machine learning based approaches to earthquake prediction was studied. The aim is to systematize the methods used and analyze the main trends in making predictions. We believe that this research will be useful and encouraging for both earthquake scientists and beginner researchers in this field.
KW - Data mining
KW - Earthquake prediction
KW - Neural networks
KW - Seismology
KW - Time series
UR - http://www.scopus.com/inward/record.url?scp=85067196962&partnerID=8YFLogxK
UR - http://ceur-ws.org/Vol-2372/SEIM_2019_paper_31.pdf
UR - http://ceur-ws.org/Vol-2372/
M3 - Conference contribution
AN - SCOPUS:85067196962
T3 - CEUR Workshop Proceedings
SP - 25
EP - 32
BT - Proceedings of the FourthConference on Software Engineering and Information Management (SEIM 2019)
A2 - Litvinov, Y.
A2 - Trifonov, P.
PB - RWTH Aahen University
Y2 - 13 April 2019
ER -
ID: 48947470