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A Deep-Learning Approach for Reducing the Probability of False Alarms in Smartphone-Based Earthquake Early Warning Systems

In: Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science

Author

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  • Frank Yannick Massoda Tchoussi

    (University of Bergamo)

  • Francesco Finazzi

    (University of Bergamo)

Abstract

Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements, thus the need to control the probability of false alarm and the probability of missed detection. This chapter proposes a deep-learning approach to address this challenge. The methodology is tested using data coming from the Earthquake Network citizen science initiative, which implements a global smartphone-based EEWS.

Suggested Citation

  • Frank Yannick Massoda Tchoussi & Francesco Finazzi, 2024. "A Deep-Learning Approach for Reducing the Probability of False Alarms in Smartphone-Based Earthquake Early Warning Systems," Springer Books, in: Sven Knoth & Yarema Okhrin & Philipp Otto (ed.), Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science, pages 425-440, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-69111-9_20
    DOI: 10.1007/978-3-031-69111-9_20
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