Speaker
Description
R. de Jesus1,2,3, G. Yang1, A.A. Pimenta3, A.J. de Abreu3,4, L.F.R. Vital1,2,3, O.M. Adebayo3, V.F. Andrioli3, A.M. Santos3, P.P. Batista3, M.P.P. Martins3, L.C.A. Resende3, C.S. Carmo1,2,3, D. Medeiros1,2,3, I.S. Batista3, K. Venkatesh5, P.R. Fagundes6, C. Wang1, H. Li1, Z. Liu1
1State Key Laboratory of Space Weather, National Space Science Center/Chinese Academy of Sciences, Beijing, China, 2China-Brazil Joint Laboratory for Space Weather, National Space Science Center, São José dos Campos, Brazil, 3National Institute for Space Research (INPE), São José dos Campos, Brazil, 4Instituto Tecnológico de Aeronáutica (ITA), Divisão de Ciências Fundamentais, São José dos Campos, SP, Brazil, 5Physical Research Laboratory, Ahmedabad, India, 6Universidade do Vale do Paraíba /IP & D, São José dos Campos, SP, Brazil
Abstract
The scientific community has long been interested in developing models for ionospheric forecasting. Several studies have applied machine learning to develop more sophisticated models. In this work, machine learning models have been developed to predict vertical total electron content (VTEC) at Cachoeira Paulista (CHPI; 23ºS, 45ºW), Brazil. For this, the Random Forest (RF) and Support Vector Regression (SVR) from machine learning algorithms will be used. The GNSS and meteor radar observations from CHPI during 2019-2020 are used in this investigation. The performance of the machine learning models is compared with that of Seasonal Autoregressive Integrated Moving Average (SARIMA). Models performance is validated using Root Mean Square Error (RMSE) and Mean Square Error (MSE). Initially, the input parameters considered in the models were Dst index and F10.7 solar flux. Preliminary results showed that the SARIMA model provides slightly better VTEC forecasts than the RF and SVR models. This result suggests that additional input parameters may be necessary to improve machine learning models. Therefore, wind data derived from the meteor radar, as well as days of the year (DOY) and local time will be incorporated as additional predictors.