IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v111y2022i2d10.1007_s11069-021-05093-x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-021-05093-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-021-05093-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hai-Bang Ly & Tien-Thinh Le & Huong-Lan Thi Vu & Van Quan Tran & Lu Minh Le & Binh Thai Pham, 2020. "Computational Hybrid Machine Learning Based Prediction of Shear Capacity for Steel Fiber Reinforced Concrete Beams," Sustainability, MDPI, vol. 12(7), pages 1-34, March.
    2. João Vieira & Maria Conceição Cunha, 2017. "Nested Optimization Approach for the Capacity Expansion of Multiquality Water Supply Systems under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(4), pages 1381-1395, March.
    3. Marijana Hadzima-Nyarko & Anamarija Rabi & Marija Šperac, 2014. "Implementation of Artificial Neural Networks in Modeling the Water-Air Temperature Relationship of the River Drava," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1379-1394, March.
    4. Phong Tung Nguyen & Duong Hai Ha & Abolfazl Jaafari & Huu Duy Nguyen & Tran Van Phong & Nadhir Al-Ansari & Indra Prakash & Hiep Van Le & Binh Thai Pham, 2020. "Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam," IJERPH, MDPI, vol. 17(7), pages 1-20, April.
    5. Shinyoung Kwag & Daegi Hahm & Minkyu Kim & Seunghyun Eem, 2020. "Development of a Probabilistic Seismic Performance Assessment Model of Slope Using Machine Learning Methods," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    6. Gong, Xinghui & Zhang, Hongbo & Ren, Chongfeng & Sun, Dongyong & Yang, Jiantao, 2020. "Optimization allocation of irrigation water resources based on crop water requirement under considering effective precipitation and uncertainty," Agricultural Water Management, Elsevier, vol. 239(C).
    7. Jyh-Woei Lin, 2020. "Researching significant earthquakes in Taiwan using two back-propagation neural network models," 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. 103(3), pages 3563-3590, September.
    8. Binh Thai Pham & Chongchong Qi & Lanh Si Ho & Trung Nguyen-Thoi & Nadhir Al-Ansari & Manh Duc Nguyen & Huu Duy Nguyen & Hai-Bang Ly & Hiep Van Le & Indra Prakash, 2020. "A Novel Hybrid Soft Computing Model Using Random Forest and Particle Swarm Optimization for Estimation of Undrained Shear Strength of Soil," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    9. Hakan Tongal & Martijn J. Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.
    10. D.-A. An-Vo & S. Mushtaq & T. Nguyen-Ky & J. Bundschuh & T. Tran-Cong & T. Maraseni & K. Reardon-Smith, 2015. "Nonlinear Optimisation Using Production Functions to Estimate Economic Benefit of Conjunctive Water Use for Multicrop Production," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(7), pages 2153-2170, May.
    11. Hakan Tongal & Martijn Booij, 2016. "A Comparison of Nonlinear Stochastic Self-Exciting Threshold Autoregressive and Chaotic k-Nearest Neighbour Models in Daily Streamflow Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(4), pages 1515-1531, March.
    12. Zahra Kayhomayoon & Sami Ghordoyee Milan & Naser Arya Azar & Pete Bettinger & Faezeh Babaian & Abolfazl Jaafari, 2022. "A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    13. Mohamed Derbeli & Cristian Napole & Oscar Barambones, 2021. "Machine Learning Approach for Modeling and Control of a Commercial Heliocentris FC50 PEM Fuel Cell System," Mathematics, MDPI, vol. 9(17), pages 1-18, August.
    14. Kaghazchi, Afsaneh & Hashemy Shahdany, S. Mehdy & Roozbahani, Abbas, 2021. "Simulation and evaluation of agricultural water distribution and delivery systems with a Hybrid Bayesian network model," Agricultural Water Management, Elsevier, vol. 245(C).
    15. Danial Jahed Armaghani & Panagiotis G. Asteris & Behnam Askarian & Mahdi Hasanipanah & Reza Tarinejad & Van Van Huynh, 2020. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness," Sustainability, MDPI, vol. 12(6), pages 1-17, March.
    16. Abdüsselam Altunkaynak, 2014. "Predicting Water Level Fluctuations in Lake Michigan-Huron Using Wavelet-Expert System Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(8), pages 2293-2314, June.
    17. João R. B. Paiva & Alana S. Magalhães & Pedro H. F. Moraes & Júnio S. Bulhões & Wesley P. Calixto, 2021. "Stability Metric Based on Sensitivity Analysis Applied to Electrical Repowering System," Energies, MDPI, vol. 14(22), pages 1-21, November.
    18. C. Dai & Y. P. Cai & W. T. Lu & H. Liu & H. C. Guo, 2016. "Conjunctive Water Use Optimization for Watershed-Lake Water Distribution System under Uncertainty: a Case Study," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(12), pages 4429-4449, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:111:y:2022:i:2:d:10.1007_s11069-021-05093-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.