期刊
SENSORS
卷 20, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/s20030800
关键词
earthquake detection; time-series features; Internet of Things; machine learning
资金
- National Disaster Management Research Institute [2019-02-02]
- Basic Science Research Program through the National Research Foundation of Korea(NRF) - the Ministry of Education [NRF-2017R1C1B5075658]
- BK21 Plus project (SW Human Resource Development Program for Supporting Smart Life) - Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea [21A20131600005]
Detecting earthquakes using smartphones or IoT devices in real-time is an arduous and challenging task, not only because it is constrained with the hard real-time issue but also due to the similarity of earthquake signals and the non-earthquake signals (i.e., noise or other activities). Moreover, the variety of human activities also makes it more difficult when a smartphone is used as an earthquake detecting sensor. To that end, in this article, we leverage a machine learning technique with earthquake features rather than traditional seismic methods. First, we split the detection task into two categories including static environment and dynamic environment. Then, we experimentally evaluate different features and propose the most appropriate machine learning model and features for the static environment to tackle the issue of noisy components and detect earthquakes in real-time with less false alarm rates. The experimental result of the proposed model shows promising results not only on the given dataset but also on the unseen data pointing to the generalization characteristics of the model. Finally, we demonstrate that the proposed model can be also used in the dynamic environment if it is trained with different dataset.
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