Tsunami arrival time detection system applicable to discontinuous time series data with outliers

Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after t...

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Bibliographic Details
Published in:Natural hazards and earth system sciences 2016-12, Vol.16 (12), p.2603-2622
Main Authors: Lee, Jun-Whan, Park, Sun-Cheon, Lee, Duk Kee, Lee, Jong Ho
Format: Article
Language:English
Subjects:
Alarms
Algorithms
Data
Data acquisition
Data analysis
Detection
Detection equipment
Earthquakes
False alarms
Methods
Neural networks
Outliers (statistics)
Real time
Time series
Timing
Tsunamis
Water levels
Wavelet transforms
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Summary:Timely detection of tsunamis with water level records is a critical but logistically challenging task because of outliers and gaps. Since tsunami detection algorithms require several hours of past data, outliers could cause false alarms, and gaps can stop the tsunami detection algorithm even after the recording is restarted. In order to avoid such false alarms and time delays, we propose the Tsunami Arrival time Detection System (TADS), which can be applied to discontinuous time series data with outliers. TADS consists of three algorithms, outlier removal, gap filling, and tsunami detection, which are designed to update whenever new data are acquired. After calibrating the thresholds and parameters for the Ulleung-do surge gauge located in the East Sea (Sea of Japan), Korea, the performance of TADS was discussed based on a 1-year dataset with historical tsunamis and synthetic tsunamis. The results show that the overall performance of TADS is effective in detecting a tsunami signal superimposed on both outliers and gaps.
ISSN:1684-9981
1561-8633
1684-9981
DOI:10.5194/nhess-16-2603-2016