An Investigation of Optimal Machine Learning Methods for the Prediction of ROTI

作者：Fulong XU,Zishen LI,Kefei ZHANG,Ningbo WANG,Suqin WU,Andong HU,Lucas Holden

摘要：The rate of the total electron content(TEC)change index(ROTI)can be regarded as an effective indicator of the level of ionospheric scintillation,in particular in low and high latitude regions.An accurate prediction of the ROTI is essential to reduce the impact of the ionospheric scintillation on earth observation systems,such as the global navigation satellite systems.However,it is difficult to predict the ROTI with high accuracy because of the complexity of the ionosphere.In this study,advanced machine learning methods have been investigated for ROTI prediction over a station at high-latitude in Canada.These methods are used to predict the ROTI in the next 5 minutes using the data derived from the past 15 minutes at the same location.Experimental results show that the method of the bidirectional gated recurrent unit network(BGRU)outperforms the other six approaches tested in the research.It is also confirmed that the RMSEs of the predicted ROTI using the BGRU method in all four seasons of 2017 are less than 0.05 TECU/min.It is demonstrated that the BGRU method exhibits a high level of robustness in dealing with abrupt solar activities.

發文機構：School of Environment Science and Spatial Information Satellite Positioning for Atmosphere Aerospace Information Research Institute Centrum Wiskunde&Informatica（CWI）

關鍵詞：machinelearningROTIpredictionionosphericscintillationhigh-latituderegion

分類號： P35[天文地球—空間物理學]