电讯技术2025，Vol.65Issue(6)：930-938,9.DOI:10.20079/j.issn.1001-893x.240703002
基于TDBO-XGB模型的电离层闪烁预测方法
Ionospheric Scintillation Prediction Based on TDBO-XGB Model
摘要
Abstract
Responding to the difficulty of predicting ionospheric scintillation and the low accuracy of prediction models,a TDBO-XGB ionospheric scintillation prediction model based on XGBoost machine learning model(XGB)combined with the Tent Dung Beetle Optimizer(TDBO)of the chaos mapping is proposed.The background ionospheric parameters associated with ionospheric scintillation five hours before sunset at HKOH station in Hong Kong from January 1,2020 to March 21,2024 are used to model the prediction of whether an ionospheric scintillation event occurs three hours after sunset,and the model is used to predict and analyze six different combinations of background ionospheric parameters.The results show that the accuracy of the TDBO-XGB model in predicting the occurrence of ionospheric scintillation reaches 93.72%,which is 1.94%higher than that of the XGB model with a single default parameter;in the prediction of scintillation using different combinations of parameters as the input data,the parameter that characterizes the solar activity enhances the prediction results of the ionospheric scintillation prediction model,and the effect of using the TEC data along the longitude line in the north and south hemispheres east of the site is significant for the prediction of ionospheric scintillation.关键词
电离层闪烁预测/机器学习/优化算法/混沌映射Key words
ionospheric scintillation/prediction of ionospheric scintillation/machine learning/optimization algorithm/chaos mapping分类
计算机与自动化引用本文复制引用
黎霏,孙希延,纪元法,梁文斌,嵇建波..基于TDBO-XGB模型的电离层闪烁预测方法[J].电讯技术,2025,65(6):930-938,9.基金项目
国家自然科学基金资助项目(U23A20280,62161007,62061010) （U23A20280,62161007,62061010）
广西科技厅项目(桂科AA23062038,桂科AD22080061,桂科AB23026120) （桂科AA23062038,桂科AD22080061,桂科AB23026120）
广西研究生教育创新计划项目(YCSW2024329) （YCSW2024329）
"认知无线电与信息处理"教育部重点实验室2023年主任基金项目(CRKL220102) （CRKL220102）
