Name
Abstract | ||
|---|---|---|
Earthquakes around the world have been a cause of major destruction and loss of life and property. An early detection and prediction system using machine learning classification models can prove to be very useful for disaster management teams. The earthquake stations continuously collect data even when there is no event. From this data, we need to distinguish earthquake and non-earthquake. Machine learning techniques can be used to analyze continuous time series data to detect earthquakes effectively. Furthermore, the earthquake data can be used to predict the P-wave and S-wave arrival times. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BigDataService.2018.00017 | 2018 IEEE Fourth International Conference on Big Data Computing Service and Applications (BigDataService) |
Keywords | Field | DocType |
Earthquake,Seismic waveform,S and P waves,Machine learning,Epicenter,Noise removal,obspy,SVM,Decision Tree,Random forest | Decision tree,Time series,Data mining,Computer science,Support vector machine,Emergency management,Feature extraction,Artificial intelligence,Data classification,Hidden Markov model,Statistical classification,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-5120-9 | 2 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenrui Li | 1 | 58 | 8.98 |
| Nishita Narvekar | 2 | 2 | 0.35 |
| Nakshatra Nakshatra | 3 | 2 | 0.35 |
| Nitisha Raut | 4 | 2 | 0.35 |
| Birsen Sirkeci | 5 | 2 | 0.35 |
| Jerry Gao | 6 | 168 | 20.38 |