Case Study of MEMS-Based Earthquake Early Warning With P-ALERT System: A Literature Review
DOI:
https://doi.org/10.63581/JoCPES.v6i1.01Keywords:
Earthquake, Early Warning System, MEMS (Micro-Electromechanical Systems), MEMS accelerometersAbstract
Earthquakes pose significant threats to infrastructure and human life,
emphasizing the urgent need for effective Earthquake Early Warning Systems (EEWS). Traditional EEWS are often expensive and inaccessible for many earthquake-prone regions. This paper explores the potential of low-cost EEWS utilizing Micro-Electro-Mechanical Systems (MEMS), focusing on
the P-ALERT system developed in Taiwan. Through a literature review, this
study analyzes the architecture, performance, limitations, and prospects of
MEMS-based EEWS. The findings highlight that P-ALERT, with its affordability, dense sensor network, and rapid response capability, offers a
promising solution for localized and regional earthquake warnings. Despite
limitations such as lower data quality compared to high-end seismometers
and reliance on stable infrastructure, P-ALERT has demonstrated effectiveness during several major earthquakes. This review also provides
recommendations for enhancing the system’s performance through
decentralized processing, improved sensor algorithms, and strengthened
network security. Ultimately, this research underscores the viability of
low-cost EEWS like P-ALERT as scalable and inclusive tools for seismic
risk mitigation, particularly in developing countries.
References
M. Adhikari, D. Paton, D. Johnston, R. Prasanna, and S. T. McColl, “Modelling predictors of earthquake hazard preparedness in Nepal,” Procedia Engineering, vol. 212, pp. 910–917, 2018, doi: 10.1016/j.proeng.2018.01.117.
N. Algiriyage, R. Prasanna, K. Stock, E. E. H. Doyle, and D. Johnston, “Multi source multimodal data and deep learning for disaster response: A systematic review,” SN Computer Science, vol. 3, no. 1, p. 92, 2022, doi: 10.1007/s42979-021-00971-4.
R. M. Allen, Q. Kong, and R. Martin-Short, “The MyShake platform: A global vision for earthquake early warning,” Pure and Applied Geophysics, vol. 177, no. 4, pp. 1699–1712, 2020, doi: 10.1007/s00024-019-02337-7.
J. S. Becker et al., “Scoping the potential for earthquake early warning in Aotearoa New Zealand: A sectoral analysis of perceived benefits and challenges,” International Journal of Disaster Risk Reduction, vol. 51, p. 101765, 2020, doi: 10.1016/j.ijdrr.2020.101765.
R. Bossu, F. Finazzi, R. Steed, L. Fallou, and I. Bondár, “'Shaking in 5 Seconds!'—Performance and user appreciation assessment of the Earthquake Network smartphone‐based public earthquake early warning system,” Seismological Research Letters, vol. 93, no. 1, pp. 137–148, 2021, doi: 10.1785/0220210180.
B. A. Brooks et al., “Robust earthquake early warning at a fraction of the cost: ASTUTI Costa Rica,” AGU Advances, vol. 2, no. 3, p. e2021AV000407, 2021, doi: 10.1029/2021av000407.
V. Cascone, J. Boaga, and G. Cassiani, “Small local earthquake detection using low‐cost MEMS accelerometers: Examples in northern and central Italy,” Seismic Record, vol. 1, no. 1, pp. 20–26, 2021, doi: 10.1785/0320210007.
C. Chandrakumar, R. Prasanna, M. Stephens, and M. L. Tan, “Earthquake early warning systems based on low-cost ground motion sensors: A systematic literature review,” Frontiers in Sensors, vol. 3, p. 1020202, 2022, doi: 10.3389/fsens.2022.1020202.
E. S. Cochran et al., “Event detection performance of the PLUM earthquake early warning algorithm in southern California,” Bulletin of the Seismological Society of America, vol. 109, no. 4, pp. 1524–1541, 2019, doi: 10.1785/0120180326.
F. Franchi, A. Marotta, C. Rinaldi, F. Graziosi, and L. D’Errico, “IoT-based disaster management system on 5G uRLLC network,” in Proc. 2019 Int. Conf. on Information and Communication Technologies for Disaster Management (ICT-DM), pp. 1–4, doi: 10.1109/ICT-DM47966.2019.9032897.
H. Hong, L. Ni, and H. Sun, “Application and prospect of MEMS technology to geophysics,” in Proc. 16th IEEE Int. Conf. on Nano/Micro Engineered and Molecular Systems (NEMS), 2021, pp. 1863–1866, doi: 10.1109/NEMS51815.2021.9451455.
Y.-M. Wu, Y.-H. Lin, B.-M. Yang, et al., "Performance of the P-alert real-time shakemaps system and onsite warning during the 2025 ML6.4 Dapu earthquake," *Terrestrial, Atmospheric and Oceanic Sciences*, vol. 36, no. 3, 2025, doi: 10.1007/s44195-025-00086-w.
Y.-M. Wu, W.-T. Liang, H. Mittal, W.-A. Chao, C.-H. Lin, B.-S. Huang, and C.-M. Lin, "Performance of a low-cost earthquake early warning system (P-Alert) during the 2016 ML 6.4 Meinong (Taiwan) earthquake," *Seismological Research Letters*, vol. 87, no. 5, pp. 1050–1059, 2016, doi: 10.1785/0220160058.
Y.-M. Wu and H. Mittal, "A review on the development of earthquake warning system using low-cost sensors in Taiwan," Sensors, vol. 21, no. 22, p. 7649, Nov. 2021, doi: 10.3390/s21227649.
S. C. Patel and R. M. Allen, "The MyShake App: User experience of early warning delivery and earthquake shaking," Seismol. Res. Lett., vol. 93, no. 6, pp. 3324–3336, Nov. 2022, doi: 10.1785/0220220062.
J. Clinton et al., “Low-cost sensors for earthquake early warning systems: Challenges and perspectives,” Seismol. Res. Lett., vol. 90, no. 1, pp. 20–31, 2019, doi: 10.1785/0220180192.
M. Kühnlenz, J. Reilly, and R. M. Allen, “Using machine learning to improve earthquake early warning systems,” Sensors, vol. 21, no. 15, p. 5093, 2021, doi: 10.3390/s21155093.
S. Alam et al., “Multi-hazard early warning systems for effective disaster risk reduction,” Int. J. Disaster Risk Reduct., vol. 60, p. 102267, 2021, doi: 10.1016/j.ijdrr.2021.102267.
Downloads
Published
Data Availability Statement
The data analyzed during this study are included in this published article as a literature review. They are also available from the cited references.
Issue
Section
License
Copyright (c) 2026 Journal of Computation Physics and Earth Science (JoCPES)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
License Terms
This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0). By submitting and publishing your work in this journal, you agree to the following terms:
-
Attribution: You must give appropriate credit to the original author(s) and provide a link to the license. You may do so in any reasonable manner, but not in any way that suggests the licensor(s) endorse you or your use.
-
ShareAlike: If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. This means any derivative works must be shared alike under CC BY-SA 4.0.
-
Non-Exclusive Distribution: Authors retain copyright of their work, and publishing in this journal does not transfer ownership. Authors grant the journal a non-exclusive, worldwide, and royalty-free license to publish, share, and distribute the work under the CC BY-SA 4.0 license.
-
Commercial and Non-Commercial Use: The work can be used for both commercial and non-commercial purposes, provided that the original author(s) are properly credited, and derivative works are distributed under the same terms.
-
No Warranty: The work is provided "as is", and the authors or the journal make no warranties regarding the accuracy, completeness, or reliability of the content.
-
Derivative Works: You may create derivative works of the material, but such works must be licensed under the same CC BY-SA 4.0 license and must attribute the original author(s) appropriately.
-
No Additional Restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
For more information, please refer to the full terms of the Creative Commons Attribution-ShareAlike 4.0 International License.




