@Article{LimaStepPaulAbdu:2015:PrLeIo,
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 (2.52°S, 44.3°W; dip latitude: ~2.5°S) 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 = "16 jun. 2024"
}