Murti, Muhammad Ary and Junior, Rio and Ahmed, Ali Najah and Elshafie, Ahmed (2022) Earthquake multi-classification detection based velocity and displacement data filtering using machine learning algorithms. Scientific Reports, 12 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-25098-1.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | Indonesia Ministry of Education, Culture, Research, and Technology [019/SP2H/RT-JAMAK/LL4/2022] |
| Uncontrolled Keywords: | Earthquake; Natural disasters; Detection |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QE Geology T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 18 Sep 2023 02:17 |
| Last Modified: | 18 Sep 2023 02:17 |
| URI: | http://eprints.um.edu.my/id/eprint/41282 |
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