{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:51:47Z","timestamp":1762300307336,"version":"build-2065373602"},"reference-count":69,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System","award":["CEMEE2022G0201"],"award-info":[{"award-number":["CEMEE2022G0201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To further improve the prediction accuracy of the critical frequency of the ionospheric F2 layer (foF2), we use the machine learning method (ML) to establish an explanatory dynamic model to predict foF2. Firstly, according to the ML modeling process, the three elements of establishing a prediction model of foF2 and four problems to be solved are determined, and the idea and concrete steps of model building are determined. Then the data collection is explained in detail, and according to the modeling process, foF2 dynamic change mapping and its parameters are determined in turn. Finally, the established model is compared with the International Reference Ionospheric model (IRI-2016) and the Asian Regional foF2 Model (ARFM) to verify the validity and reliability. The results show that compared with the IRI-URSI, IRI-CCIR, and ARFM models, the statistical average error of the established model decreased by 0.316 MHz, 0.132 MHz, and 0.007 MHz, respectively. Further, the statistical average relative root-mean-square error decreased by 9.62%, 4.05%, and 0.15%, respectively.<\/jats:p>","DOI":"10.3390\/rs15051256","type":"journal-article","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T01:59:10Z","timestamp":1677463150000},"page":"1256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["An Explainable Dynamic Prediction Method for Ionospheric foF2 Based on Machine Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-8946","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"first","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"},{"name":"Qingdao Institute for Ocean Technology, Tianjin University, Qingdao 266200, China"},{"name":"Shandong Engineering Technology Research Center of Ocean Information Awareness and Transmission, Qingdao 266200, China"}]},{"given":"Qiao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2921-3558","authenticated-orcid":false,"given":"Yafei","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"},{"name":"Qingdao Institute for Ocean Technology, Tianjin University, Qingdao 266200, China"}]},{"given":"Yiran","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4173-6060","authenticated-orcid":false,"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"},{"name":"Qingdao Institute for Ocean Technology, Tianjin University, Qingdao 266200, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1928","DOI":"10.1002\/grl.50448","article-title":"Annual\/semiannual variation of the ionosphere","volume":"40","author":"Qian","year":"2013","journal-title":"Geophys. 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