A New Approach for Low-Latitude Ionospheric Scintillation Prediction
Abstract
The prediction of ionospheric scintillation is a current research topic. Data-oriented models have been proposed since there is not a mathematical model able to simulate the complex ionospheric mechanisms for the prediction of scintillation. Data-oriented models employ machine learning algorithms that are trained with known, post-mortem data, and then perform predictions from new data. An ensemble method based on sampling boosting applied for a set of decision trees is employed for the prediction of scintillation in a single low-latitude location in Brazil. The prediction was performed considering two classes, occurrence or absence of scintillation. The method uses as input temporal series of the scintillation index S4, the total electron content (TEC), and some geomagnetic and solar indexes for that location. Data encompasses the summer months of the previous solar cycle years (2010-2018), and it was split into mutually-exclusive training and validation/test sets. Prediction performance was promising, showing a potential to be developed for operational use. Data limitations related to time series extension, or also to a balanced distribution of samples/instances covering both classes, since scintillation occurrences are usually scarce, can be further developed as new data are available.
Keywords
Full Text:
PDFReferences
Abdu M. A. 2019, Day-to-day and short-term variabilities in the equatorial plasma bubble/spread F irregularity seeding and development. In: Progress in Earth and Planetary Science 6.1, 11, 11. DOI: 10.1186/s40645-019-0258-1
Barandas M., Folgado D., Fernandes L., Santos S., Abreu M., Bota P., Liu H., Schultz T., and Gamboa H. 2020, TSFEL: Time Series Feature Extraction Library. In: SoftwareX 11, 100456. DOI: 10.1016/j.softx.2020.100456
Béniguel Y. and Hamel P. 2011, A global ionosphere scintillation propagation model for equatorial regions. In: J. Space Weather Space Clim. 1.1, A04. DOI: 10.1051/swsc/2011004
Camporeale E. 2019, The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting. In: Space Weather 17, 1166–1207. DOI: 10.1029/2018SW002061
Chen T. and Guestrin C. 2016, XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. San Francisco, California, USA: ACM, 785–794. DOI: 10.1145/2939672.2939785
Christ M., Kempa-Liehr A., and Feindt M. 2016, Distributed and parallel time series feature extraction for industrial big data applications. In: ArXiv abs/1610.07717
Christ M., Braun N., Neuffer J., and Kempa-Liehr A. W. 2018, Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package). In: Neurocomputing 307, 72–77. DOI: 10.1016/j.neucom.2018.03.067
De Paula E. R., Moraes A. O., Abdu M. A., Sobral J. H. A., Batista I. S., Kherani E. A., Takahashi H., and Costa E. 2021, Long term studies on scintillation and plasma bubbles. In: P. Wilkinson, P. S. Cannon, and W. R. Stone. (Ed.). 100 Years of the International Union of Radio Science. Gent, Belgium: URSI Press. Chap. 29, 557–562
Dorogush A. V., Ershov V., and Gulin A. 2018, CatBoost: gradient boosting with categorical features support. In: Computing Research Repository - CoRR abs/1810.11363. DOI: 10.48550/arXiv.1810.11363
Lemaître G., Nogueira F., and Aridas C. K. 2017, Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. In: Journal of Machine Learning Research 18.17, 1–5
Lima G. R. T. D., Stephany S., De Paula E. R., Batista I. S., Abdu M. A., Rezende L. F. C., Aquino M. G. S., and Dutra A. P. S. 2014, Correlation analysis between the occurrence of ionospheric scintillation at the magnetic equator and at the southern peak of the equatorial ionization anomaly. In: Space Weather 12.6, 406–416. DOI: 10.1002/2014SW001041
Lima G. R. T. D., Stephany S., De Paula E. R., Batista I. S., and Abdu M. A. 2015, Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network. In: Space Weather 13.8, 446–457. DOI: 10.1002/2015SW001182
Mcgranaghan R. M., MANNUCCI A. J., WILSON B., MATTMANN C. A., and CHADWICK R. 2018, New Capabilities for Prediction of High-Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning. In: Space Weather 16.11, 1817–1846. DOI: 10.1029/2018SW002018
Otsuka Y., Ogawa T., Saito A., Tsugawa T., Fukao S., and Miyazaki S. 2002, New technique for mapping of total electron content using GPS network in Japan. In: Earth and Planetary Science Letters 54, 63–70. DOI: 10.1186/BF03352422
Prokhorenkova L., Gusev G., Vorobev A., Dorogush A. V., and Gulin A. 2018, CatBoost: Unbiased Boosting with Categorical Features. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. NIPS’18. Montreal, Canada: Curran Associates Inc., 6639– 6649
Retterer J. M. 2010, Forecasting low-latitude radio scintillation with 3-D ionospheric plume models: 2. Scintillation calculation. In: Journal of Geophysical Research: Space Physics 115.A3. DOI: 10.1029/2008JA013840
Rezende L. F. C., Paula E. R. De, Stephany S., Kantor I. J., Muella M. T. A. H., Siqueira P. M. De, and Correa K. S. 2010, Survey and prediction of the ionospheric scintillation using data mining techniques. In: Space Weather 8.6, S06D09. DOI: 10.1029/2009SW000532
Sousa do Carmo C. De 2018, Estudo de diferentes técnicas para o cálculo do conteúdo eletrônico total absoluto na ionosfera equatorial e de baixas latitudes. (02.21.19.14-TDI). Master Dissertation (Geofísica Espacial/Ciências do Ambiente Solar-Terrestre). São José dos Campos, Brazil: Instituto Nacional de Pesquisas Espaciais (INPE), 149 pp.
Vani B. C. 2018, Investigações sobre modelagem, mitigação e predição de cintilação ionosférica na região brasileira. Ph.D. Thesis. Presidente Prudente, Brazil: Universidade Estadual Paulista. Faculdade de Ciências e Tecnologia, Campus Presidente Prudente, 182 pp.
Wernik A. W., Alfonsi L., and Materassi M. 2007, Scintillation modeling using in situ data. In: Radio Science 42.1, RS1002. DOI: 10.1029/2006RS003512
Yokoyama T. 2017, A review on the numerical simulation of equatorial plasma bubbles toward scintillation evaluation and forecasting. In: Progress in Earth and Planetary Science 4.37. DOI: 10.1186/s40645-017-0153-6
Zhao X., Li G., Xie H., Hu L., Sun W., Yang S., Li Y., Ning B., and Takahashi H. 2021, The Prediction of Day-to-Day Occurrence of Low Latitude Ionospheric Strong Scintillation Using Gradient Boosting Algorithm. In: Space Weather 19.12, e2021SW002884. DOI: 10.1029/2021SW002884
DOI: http://dx.doi.org/10.22564/brjg.v40i4.2187
This work is licensed under a Creative Commons Attribution 4.0 International License.
a partir do v.37n.4 (2019) até o presente
v.15n.1 (1997) até v.37n.3 (2019)
Brazilian Journal of Geophysics - BrJG
Sociedade Brasileira de Geofísica - SBGf
Av. Rio Branco 156 sala 2509
Rio de Janeiro, RJ, Brazil
Phone/Fax: +55 21 2533-0064
E-mail: editor@sbgf.org.br
Since 2022, the BrJG publishes all content under Creative Commons CC BY license. All copyrights are reserved to authors.
