@Article{BenoitPetr:2021:EvF1Su,
               author = "Benoit, Andres Gilberto Machado da Silva and Petry, Adriano",
          affiliation = "{Universidade Federal de Santa Maria (UFSM)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Evaluation of f10.7, sunspot number and photon flux data for 
                         ionosphere tec modeling and prediction using machine learning 
                         techniques",
              journal = "Atmosphere",
                 year = "2021",
               volume = "12",
               number = "9",
                pages = "e1202",
                month = "Sept.",
             keywords = "Ionosphere modeling, Machine learning, Regression model, Total 
                         electron content.",
             abstract = "Considering the growing volumes and varieties of ionosphere data, 
                         it is expected that automation of analytical model building using 
                         modern technologies could lead to more accurate results. In this 
                         work, machine learning techniques are applied to ionospheric 
                         modeling and prediction using sun activity data. We propose Total 
                         Electron Content (TEC) spectral analysis, using discrete cosine 
                         transform (DCT) to evaluate the relation to the solar features 
                         F10.7, sunspot number and photon flux data. The ionosphere 
                         modeling procedure presented is based on the assessment of a 
                         six-year period (20142019) of data. Different multi-dimension 
                         regression models were considered in experiments, where each 
                         geographic location was independently evaluated using its DCT 
                         frequency components. The features correlation analysis has shown 
                         that 5-year data seem more adequate for training, while learning 
                         curves revealed overfitting for polynomial regression from the 4th 
                         to 7th degrees. A qualitative evaluation using reconstructed TEC 
                         maps indicated that the 3rd degree polynomial regression also 
                         seems inadequate. For the remaining models, it can be noted that 
                         there is seasonal variation in root-mean-square error (RMSE) 
                         clearly related to the equinox (lower error) and solstice (higher 
                         error) periods, which points to possible seasonal adjustment in 
                         modeling. Elastic Net regularization was also used to reduce 
                         global RMSE values down to 2.80 TECU for linear regression.",
                  doi = "10.3390/atmos12091202",
                  url = "http://dx.doi.org/10.3390/atmos12091202",
                 issn = "2073-4433",
             language = "en",
           targetfile = "benoit_evaluation.pdf",
        urlaccessdate = "13 maio 2024"
}