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

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  • Jyh-Woei Lin

    (Nanjing University of Information Science & Technology
    Southern Taiwan University of Science and Technology)

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.

Suggested Citation

  • Jyh-Woei Lin, 2022. "Generalized two-dimensional principal component analysis and two artificial neural network models to detect traveling ionospheric disturbances," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(2), pages 1245-1270, March.
    • Handle: RePEc:spr:nathaz:v:111:y:2022:i:2:d:10.1007_s11069-021-05093-x
    DOI: 10.1007/s11069-021-05093-x
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    References listed on IDEAS

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    1. Yu Chen & Liang Chang & Chun Huang & Hone Chu, 2013. "Applying Genetic Algorithm and Neural Network to the Conjunctive Use of Surface and Subsurface Water," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(14), pages 4731-4757, November.
    2. Khawla Seddiki & Philippe Saudemont & Frédéric Precioso & Nina Ogrinc & Maxence Wisztorski & Michel Salzet & Isabelle Fournier & Arnaud Droit, 2020. "Cumulative learning enables convolutional neural network representations for small mass spectrometry data classification," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    3. Dong Van Dao & Hojjat Adeli & Hai-Bang Ly & Lu Minh Le & Vuong Minh Le & Tien-Thinh Le & Binh Thai Pham, 2020. "A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation," Sustainability, MDPI, vol. 12(3), pages 1-22, January.
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