author = "Lima, Glauston Roberto Teixeira de and Stephany, Stephan and 
                         Paula, Eurico Rodrigues de and Abdu, Mangalathayil Ali",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Prediction of the level of ionospheric scintillation at equatorial 
                         latitudes in Brazil using a neural network",
              journal = "Space Weather",
                 year = "2015",
               volume = "13",
               number = "8",
                pages = "446--457",
                month = "Aug.",
             keywords = "artificial neural network, ionospheric scintillation, prediction 
                         of occurrence of scintillation.",
             abstract = "Electron density irregularity structures, often associated with 
                         ionospheric plasma bubbles, drive amplitude and phase fluctuations 
                         in radio signals that, in turn, create a phenomenon known as 
                         ionospheric scintillation. The phenomenon occurs frequently around 
                         the magnetic equator where plasma instability mechanisms generate 
                         postsunset plasma bubbles and density depletions. A previous 
                         correlation study suggested that scintillation at the magnetic 
                         equator may provide a forecast of subsequent scintillation at the 
                         equatorial ionization anomaly southern peak. In this work, it is 
                         proposed to predict the level of scintillation over S{\~a}o 
                         Lu{\'{\i}}s (2.52S, 44.3W; dip latitude: ~2.5S) near the 
                         magnetic equator with lead time of hours but without specifying 
                         the moment at which the scintillation starts or ends. A collection 
                         of extended databases relating scintillation to ionospheric 
                         variables for S{\~a}o Lu{\'{\i}}s is employed to perform the 
                         training of an artificial neural network with a new architecture. 
                         Two classes are considered, not strong (null/weak/moderate) and 
                         strong scintillation. An innovative scheme preprocesses the data 
                         taking into account similarities of the values of the variables 
                         for the same class. A formerly proposed resampling heuristic is 
                         employed to provide a balanced number of tuples of each class in 
                         the training set. Tests were performed showing that the proposed 
                         neural network is able to predict the level of scintillation over 
                         the station on the evening ahead of the data sample considered 
                         between 17:30 and 19:00 LT. Key Points Intended to predict 
                         scintillation in the same station with antecedence of hours 
                         Employs an artificial neural network to predict the level of 
                         scintillation Introduces an innovative preprocessing for the 
                         neural network data training.",
                  doi = "10.1002/2015SW001182",
                  url = "http://dx.doi.org/10.1002/2015SW001182",
                 issn = "1542-7390",
             language = "en",
           targetfile = "lima_prediction.pdf",
        urlaccessdate = "26 jan. 2021"