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Taale, Alireza

University of British Columbia

This dissertation aims to present a full-stack concept design for an earthquake early warning platform utilizing a dense array of sensors embedded in consumer electronic devices. The proposed design includes four systems: observation, estimation, prediction, and decision systems, each with a specific type of intelligence. A novel change detection technique, specially devised for the observation system, is responsible for detecting and estimating the arrival time. Knowing the geographical location and arrival times collected from various sensors allows the platform to compute an approximate location of the event through an optimization method. The performance results on 732 ground motions indicate that the error in arrival time estimation is less than 1.5 𝑠, on average. Meanwhile, the event location estimation average error is less than 16 𝑘𝑚, with a 99% confidence level. Furthermore, a novel earthquake intensity prediction model based on a neural network structure is responsible for evaluating the size of the event. The proposed prediction model yields 57% standard error over 4691 carefully selected ground motions. In contrast, the performance results of the voting process responsible for updating the alarm status indicate the overall accuracy of 75% for the platform; that is, three-quarters of the ground motions are correctly classified, on average. However, the average false alarm rates are 20% to 30%. Accordingly, investigating the effect of the threshold value on the false alarm rates illustrates that consideration of the practicality in the design concept allows the selection of an optimal alarm threshold. Utilizing the Internet of Things (IoT) infrastructure for earthquake early warning is a compelling and challenging alternative compared to conventional platforms. Future works require collaboration between academia and industry to devise guidelines for reconfiguring IoT infrastructure and improving the performance of individual earthquake early warning systems.

Text

2021-12-21

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/80491

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/