{"title":"利用神经网络和经验模型预测进行顶层电子密度建模","authors":"S. Dutta, M. B. Cohen","doi":"10.1029/2023sw003501","DOIUrl":null,"url":null,"abstract":"We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical-Canadian High Arctic Ionospheric Model (E-CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite-years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E-CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid-latitude performance with absolute errors lower than the IRI by 2.5 × 10<sup>9</sup> to 2.5 × 10<sup>10</sup> e<sup>−</sup>/m<sup>3</sup>, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 10<sup>9</sup> e<sup>−</sup>/m<sup>3</sup> at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E-CHAIM.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions\",\"authors\":\"S. Dutta, M. B. Cohen\",\"doi\":\"10.1029/2023sw003501\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical-Canadian High Arctic Ionospheric Model (E-CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite-years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E-CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid-latitude performance with absolute errors lower than the IRI by 2.5 × 10<sup>9</sup> to 2.5 × 10<sup>10</sup> e<sup>−</sup>/m<sup>3</sup>, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 10<sup>9</sup> e<sup>−</sup>/m<sup>3</sup> at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E-CHAIM.\",\"PeriodicalId\":22181,\"journal\":{\"name\":\"Space Weather\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2023-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Space Weather\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1029/2023sw003501\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Space Weather","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2023sw003501","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
我们用改进的机器学习(ML)模型来模拟电离层顶部的电子密度,并将其与现有的经验模型,特别是国际参考电离层(IRI)和经验-加拿大北极高电离层模型(E-CHAIM)进行比较。在之前的工作中,我们开发了一个人工神经网络(NN),并根据两个太阳周期的国防气象卫星计划数据(113 个卫星年)以及全球驱动因素和指数对其进行了训练,以预测顶部电子密度。在本文中,我们将重点介绍对 NN 所做的改进,并详细比较新模型与 E-CHAIM 和 IRI 在位置、地磁条件、年度时间和太阳当地时间方面的函数关系。我们讨论了精度和准确度指标,以更好地了解模型的优缺点。更新后的神经网络改善了中纬度的性能,绝对误差比 IRI 低 2.5 × 109 到 2.5 × 1010 e-/m3,在干扰地磁条件下的性能略有改善,与 IRI 相比,在高 Kp 时绝对误差减少了约 2.5 × 109 e-/m3,与 E-CHAIM 相比,高 Kp 百分比误差减少了 50%。
Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions
We model the electron density in the topside of the ionosphere with an improved machine learning (ML) model and compare it to existing empirical models, specifically the International Reference Ionosphere (IRI) and the Empirical-Canadian High Arctic Ionospheric Model (E-CHAIM). In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite-years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E-CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. We discuss precision and accuracy metrics to better understand model strengths and weaknesses. The updated neural network shows improved mid-latitude performance with absolute errors lower than the IRI by 2.5 × 109 to 2.5 × 1010 e−/m3, modestly improved performance in disturbed geomagnetic conditions with absolute errors reduced by about 2.5 × 109 e−/m3 at high Kp compared to the IRI, and high Kp percentage errors reduced by >50% when compared to E-CHAIM.