.png){kind=icon}
返回
Predicting Equatorial Ionospheric Convective Instability Using Machine Learning
.png){kind=icon}
OA
AI
DOI：10.1029/2023SW003505.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The numerical forecast methods used to predict ionospheric convective plasma instabilities associated with Equatorial Spread-F (ESF) have limited accuracy and are often computationally expensive. We test whether it is possible to bypass first-principle numeric simulations and forecast irregularities using machine learning models. The data are obtained from the incoherent scatter radar at the Jicamarca Radio Observatory located in Lima, Peru. Our models map vertical plasma drifts, time, and solar activity to the occurrence and location of clusters of echoes telltale of ionospheric irregularities. Our results show that these models are capable of identifying the predictive power of the tested inputs, obtaining accuracies around 75%.
Keyword:
machine learning
equatorial spread F
forecasting
neural networks
random forests
ionospheric irregularities
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
S
IF:
3.5
论文数:
2.1K
被引数:
5.5K
机构
引用论文
.png){kind=icon}
分享收藏分享收藏More time for judo matches? Analysis of type of techniques, time, scores, and penalties in the Tokyo 2020 Olympic Games更多时间进行柔道比赛？东京2020奥运会技术类型、时间、得分和处罚分析OAAI
OAAI分享收藏
查看更多内容