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Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network

https://doi.org/10.1002/2015sw001182

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/2014sw001041openalex-snapshot

Correlation analysis between the occurrence of ionospheric scintillation at the magnetic equator and at the southern peak of the Equatorial Ionization Anomaly

inferredcites →https://doi.org/10.1016/0021-9169(54)90054-9openalex-snapshot

The anomalous equatorial belt in the F2-layer

inferredcites →https://doi.org/10.1016/j.cageo.2013.04.016openalex-snapshot

A new classification approach for detecting severe weather patterns

inferredcites →https://doi.org/10.1016/j.jastp.2004.07.010openalex-snapshot

Forecasting the occurrence of ionospheric scintillation activity in the equatorial ionosphere on a day-to-day basis

inferredcites →https://doi.org/10.1016/s0273-1177(03)00048-6openalex-snapshot

Equatorial anomaly effects on GPS scintillations in brazil

inferredcites →https://doi.org/10.1016/s1364-6826(00)00201-7openalex-snapshot

Outstanding problems in the equatorial ionosphere–thermosphere electrodynamics relevant to spread F

inferredcites →https://doi.org/10.1029/2004sw000095openalex-snapshot

Daytime vertical E × B drift velocities inferred from ground‐based magnetometer observations at low latitudes

inferredcites →https://doi.org/10.1029/2009sw000532openalex-snapshot

Survey and prediction of the ionospheric scintillation using data mining techniques

inferredcites →https://doi.org/10.1029/2010sw000582openalex-snapshot

A Forecasting Ionospheric Real-time Scintillation Tool (FIRST)

inferredcites →https://doi.org/10.1029/96ja00682openalex-snapshot

Linear theory and modeling of the Rayleigh‐Taylor instability leading to the occurrence of equatorial spread F

inferredcites →https://doi.org/10.1038/158234a0openalex-snapshot

Fluctuations in Cosmic Radiation at Radio-Frequencies

inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot

Learning representations by back-propagating errors

inferredcites →https://doi.org/10.1049/el:19981113openalex-snapshot

Ionospheric forecasting technique by artificialneural network

inferredcites →https://doi.org/10.1109/2.29openalex-snapshot

Neural nets for adaptive filtering and adaptive pattern recognition

inferredcites →https://doi.org/10.21528/lnlm-vol11-no2-art5openalex-snapshot

Training a Neural Network to Detect Patterns Associated with Severe Weather

inferredcites →https://doi.org/10.5194/angeo-24-1429-2006openalex-snapshot

Equatorial scintillations in relation to the development of ionization anomaly

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4682905openalex-extracted

Neural Network-Generated Simulation of Ionospheric Amplitude Scintillation Using GAN-Based Approach

inferred← citeshttps://doi.org/10.1002/2015ja021737openalex-snapshot

Nighttime morphology of vertical plasma drifts at Ouagadougou during different seasons and phases of sunspot cycles 20–22

inferred← citeshttps://doi.org/10.1002/2016ja023478openalex-snapshot

Development of intermediate‐scale structure at different altitudes within an equatorial plasma bubble: Implications for L‐band scintillations

inferred← citeshttps://doi.org/10.1002/2017ja023869openalex-snapshot

Further refinements to the spatiotemporal forecast model for L‐band scintillation based on comparison with C/NOFS observations

inferred← citeshttps://doi.org/10.1007/s10291-017-0625-6openalex-snapshot

SBAS-derived TEC maps: a new tool to forecast the spatial maps of maximum probable scintillation index over India

inferred← citeshttps://doi.org/10.1007/s10291-024-01652-4openalex-snapshot

Leveraging machine learning techniques and GPS measurements for precise TEC rate predictions

inferred← citeshttps://doi.org/10.1007/s10712-020-09613-5openalex-snapshot

Challenges to Equatorial Plasma Bubble and Ionospheric Scintillation Short-Term Forecasting and Future Aspects in East and Southeast Asia

inferred← citeshttps://doi.org/10.1007/s12145-026-02155-1openalex-snapshot

t-STEP: An interpretable model for total electron content predictions and irregularities estimations

inferred← citeshttps://doi.org/10.1016/j.asr.2023.12.026openalex-snapshot

Machine learning approach for ionospheric scintillation prediction on ROTI parameter over the African region during solar cycle 24

inferred← citeshttps://doi.org/10.1016/j.asr.2024.05.022openalex-snapshot

Machine learning approach for prediction of ionospheric irregularities on ROTI index over the Northern anomaly crest in Egypt during solar cycle 24

inferred← citeshttps://doi.org/10.1016/j.cag.2017.08.013openalex-snapshot

Visual analytics of time-varying multivariate ionospheric scintillation data

inferred← citeshttps://doi.org/10.1029/2018rs006722openalex-snapshot

VHF Scintillation and Drift Studied Using Spaced Receivers in Southern Taiwan

inferred← citeshttps://doi.org/10.1029/2018sw002018openalex-snapshot

New Capabilities for Prediction of High‐Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

inferred← citeshttps://doi.org/10.1029/2018sw002061openalex-snapshot

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

inferred← citeshttps://doi.org/10.1029/2019ja026839openalex-snapshot

Investigation of Spatiotemporal Morphology of Plasma Bubbles Based on EAR Observations

inferred← citeshttps://doi.org/10.1029/2020sw002555openalex-snapshot

On the Assessment of Daily Equatorial Plasma Bubble Occurrence Modeling and Forecasting

inferred← citeshttps://doi.org/10.1029/2021ea002164openalex-snapshot

Precursors Identification for Forecasting UHF‐Band Ionospheric Scintillation Events Over Chinese Low‐Latitude Region by Deep Learning

inferred← citeshttps://doi.org/10.1029/2021gl095561openalex-snapshot

Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps

inferred← citeshttps://doi.org/10.1029/2021sw002884openalex-snapshot

The Prediction of Day‐to‐Day Occurrence of Low Latitude Ionospheric Strong Scintillation Using Gradient Boosting Algorithm

inferred← citeshttps://doi.org/10.1029/2024sw004214openalex-snapshot

Predicting Equatorial Spread F at JICAMARCA Sector Via Supervised Machine Learning

inferred← citeshttps://doi.org/10.1029/2024sw004224openalex-snapshot

A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia

inferred← citeshttps://doi.org/10.1029/2024sw004239openalex-snapshot

ISNet: Decomposed Dynamic Spatio‐Temporal Neural Network for Ionospheric Scintillation Forecasts

inferred← citeshttps://doi.org/10.1029/2026ja035098openalex-snapshot

A Model for Predicting Ionospheric Scintillation Intensity in High Latitude Region Based on VMD‐CNN‐BiLSTM‐Attention

inferred← citeshttps://doi.org/10.1109/mlbdbi54094.2021.00028openalex-snapshot

Ionospheric prediction of low latitude airport based on Beidou Navigation System

inferred← citeshttps://doi.org/10.1109/taes.2025.3539268openalex-snapshot

Synthetic Generator for Ionospheric Amplitude Scintillation Fading Channels Using Generative Adversarial Networks

inferred← citeshttps://doi.org/10.1109/tgrs.2021.3126888openalex-snapshot

Method for Forecasting Ionospheric Electron Content Fluctuations Based on the Optical Flow Algorithm

inferred← citeshttps://doi.org/10.1109/tgrs.2023.3346449openalex-snapshot

The Short-Term Prediction of Low-Latitude Ionospheric Irregularities Leveraging a Hybrid Ensemble Model

inferred← citeshttps://doi.org/10.1109/wisee44079.2020.9262699openalex-snapshot

Distinguishing Ionospheric Scintillation from Multipath in GNSS Signals Using Bagged Decision Trees Algorithm

inferred← citeshttps://doi.org/10.11606/t.55.2018.tde-23102018-115029openalex-snapshot

Visual exploration to support the identification of relevant attributes in time-varying multivariate data

inferred← citeshttps://doi.org/10.29252/jgit.7.3.57openalex-snapshot

Combining Neural Network with Genetic Algorithm for prediction of S4 Parameter using GPS measurement

inferred← citeshttps://doi.org/10.33012/navi.615openalex-snapshot

Spatiotemporal Deep Learning Network for High-Latitude Ionospheric Phase Scintillation Forecasting

inferred← citeshttps://doi.org/10.3390/app10010381openalex-snapshot

Semi-Supervised GNSS Scintillations Detection Based on DeepInfomax

inferred← citeshttps://doi.org/10.3390/atmos17080711openalex-snapshot

A Dual-Channel XGBoost–Transformer–LSTM Fusion Method for GNSS Ionospheric Scintillation Forecasting

inferred← citeshttps://doi.org/10.3390/rs13112092openalex-snapshot

Ionospheric Scintillation Prediction on S4 and ROTI Parameters Using Artificial Neural Network and Genetic Algorithm

inferred← citeshttps://doi.org/10.3390/rs15245762openalex-snapshot

Analyzing the Ionospheric Irregularities Caused by the September 2017 Geomagnetic Storm Using Ground-Based GNSS, Swarm, and FORMOSAT-3/COSMIC Data near the Equatorial Ionization Anomaly in East Africa

inferred← citeshttps://doi.org/10.3390/rs16152757openalex-snapshot

Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America

inferred← citeshttps://doi.org/10.3390/rs17173073openalex-snapshot

Estimating Ionospheric Phase Scintillation Indices in the Polar Region from 1 Hz GNSS Observations Using Machine Learning

inferred← citeshttps://doi.org/10.3390/universe12030086openalex-snapshot

Research on Ionospheric Scintillation Effects and Prediction Model in East Asia Based on COSMIC-1 Occultation Dataset

inferred← citeshttps://doi.org/10.48550/arxiv.1910.01570openalex-snapshot

Prediction of GNSS Phase Scintillations: A Machine Learning Approach

inferred← citeshttps://doi.org/10.52547/jgit.10.3.1openalex-snapshot

Investigation of the effects of geomagnetic storms on ionospheric irregularities using the combination of ground-based GNSS and SWARM satellites data

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