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

Frank Yannick Massoda Tchoussi () and Francesco Finazzi ()
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Frank Yannick Massoda Tchoussi: University of Bergamo
Francesco Finazzi: University of Bergamo

A chapter in Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science, 2024, pp 425-440 from Springer

Abstract: 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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-69111-9_20

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DOI: 10.1007/978-3-031-69111-9_20

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