Publication:
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms

Simple item page
dc.contributor.authorMurti M.A.en_US
dc.contributor.authorJunior R.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorElshafie A.en_US
dc.contributor.authorid24734366700en_US
dc.contributor.authorid57997017600en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid16068189400en_US
dc.date.accessioned2023-05-29T09:35:59Z
dc.date.available2023-05-29T09:35:59Z
dc.date.issued2022
dc.descriptionacceleration; article; artificial neural network; decision tree; earthquake; filtration; machine learning; random forest; support vector machine; vandalism; vibration; Machine Learningen_US
dc.description.abstractEarthquake is one of the natural disasters that have a big impact on society. Currently, there are many studies on earthquake detection. However, the vibrations that were detected by sensors were not only vibrations caused by the earthquake, but also other vibrations. Therefore, this study proposed an earthquake multi-classification detection with machine learning algorithms that can distinguish earthquake and non-earthquake, and vandalism vibration using acceleration seismic waves. In addition, velocity and displacement as integration products of acceleration have been considered additional features to improve the performances of machine learning algorithms. Several machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) have been used to develop the best algorithm for earthquake multi-classification detection. The results of this study indicate that the ANN algorithm is the best algorithm to distinguish between earthquake and non-earthquake, and vandalism vibrations. Moreover, it�s also more resistant to various input features. Furthermore, using velocity and displacement as additional features has been proven to increase the performance of every model. � 2022, The Author(s).en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo21200
dc.identifier.doi10.1038/s41598-022-25098-1
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85143566590
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85143566590&doi=10.1038%2fs41598-022-25098-1&partnerID=40&md5=6fc703461ecbebdcb5fecb3e6d81cf67
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26640
dc.identifier.volume12
dc.publisherNature Researchen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleScientific Reports
dc.titleEarthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithmsen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS