Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms
acceleration; article; artificial neural network; decision tree; earthquake; filtration; machine learning; random forest; support vector machine; vandalism; vibration; Machine Learning
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Nature Research
2023
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my.uniten.dspace-266402023-05-29T17:35:59Z Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms Murti M.A. Junior R. Ahmed A.N. Elshafie A. 24734366700 57997017600 57214837520 16068189400 acceleration; article; artificial neural network; decision tree; earthquake; filtration; machine learning; random forest; support vector machine; vandalism; vibration; Machine Learning Earthquake 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). Final 2023-05-29T09:35:59Z 2023-05-29T09:35:59Z 2022 Article 10.1038/s41598-022-25098-1 2-s2.0-85143566590 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85143566590&doi=10.1038%2fs41598-022-25098-1&partnerID=40&md5=6fc703461ecbebdcb5fecb3e6d81cf67 https://irepository.uniten.edu.my/handle/123456789/26640 12 1 21200 All Open Access, Gold, Green Nature Research Scopus |
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acceleration; article; artificial neural network; decision tree; earthquake; filtration; machine learning; random forest; support vector machine; vandalism; vibration; Machine Learning |
| author2 |
24734366700 |
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24734366700 Murti M.A. Junior R. Ahmed A.N. Elshafie A. |
| format |
Article |
| author |
Murti M.A. Junior R. Ahmed A.N. Elshafie A. |
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Murti M.A. Junior R. Ahmed A.N. Elshafie A. Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| author_sort |
Murti M.A. |
| title |
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| title_short |
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| title_full |
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| title_fullStr |
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| title_full_unstemmed |
Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| title_sort |
earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms |
| publisher |
Nature Research |
| publishDate |
2023 |
| _version_ |
1806423462023004160 |
| score |
13.214268 |