Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

Data‐Driven Forecasting of Low‐Latitude Ionospheric Total Electron Content Using the Random Forest and LSTM Machine Learning Methods

Abstract In this research, we present data‐driven forecasting of ionospheric total electron content (TEC) using the Long‐Short Term Memory (LSTM) deep recurrent neural network method. The random forest machine learning method was used to perform a regression analysis and estimate the variable import...

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Bibliographic Details
Main Authors: Gebreab K. Zewdie, Cesar Valladares, Morris B. Cohen, David J. Lary, Dhanya Ramani, Gizaw M. Tsidu
Format: Article
Language:English
Published: Wiley 2021-06-01
Series:Space Weather
Subjects:
LSTM
TEC forecasting
random forest
space weather
ionosphere
Online Access:https://doi.org/10.1029/2020SW002639
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https://doi.org/10.1029/2020SW002639

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