Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances

Jyh-Woei Lin ()
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Jyh-Woei Lin: Nanjing University of Information Science & Technology

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2022, vol. 111, issue 2, No 7, 1245-1270

Abstract: Abstract A weak tsunami was induced by the 2016 Mw = 7.8 Sumatra earthquake, which occurred at 12:49 on March 2, 2016 (UTC). The epicenter was at 5.060°S, 94.170°E at a depth of 10 km. At 15.02 on March 2 (UTC), the weak tsunami (amplitude: 0.11 m) arrived at the station located at 10.40°S, 105.67°E. Two largest principal eigenvalues derived using the bilateral projection-based two-dimensional principal component analysis (B2DPCA) indicated a spatial traveling ionospheric disturbance (TID), which was caused by internal gravity waves, at 13:20 on March 2. Two largest principal eigenvalues represented another TID expanding to the southwest. These two TIDs were also determined using two back-propagation neural network (BPNN) models and two convolutional neural network models, called the BPNN-B2DPCA and CNN-B2DPCA methods, respectively. These two methods yielded the same results as the B2DPCA. Therefore, the reliability of B2DPCA was validated.

Keywords: Bilateral projection-based two-dimensional principal component analysis (B2DPCA); Traveling ionospheric disturbance (TID); Back-propagation neural network (BPNN); Convolutional neural network (CNN); BPNN-B2DPCA and CNN- B2DPCA methods (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s11069-021-05093-x

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