{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T19:10:09Z","timestamp":1771614609651,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,6]],"date-time":"2023-06-06T00:00:00Z","timestamp":1686009600000},"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>In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters.<\/jats:p>","DOI":"10.3390\/rs15122953","type":"journal-article","created":{"date-parts":[[2023,6,7]],"date-time":"2023-06-07T01:38:41Z","timestamp":1686101921000},"page":"2953","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction"],"prefix":"10.3390","volume":"15","author":[{"given":"Jiaxuan","family":"Weng","sequence":"first","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}]},{"given":"Yiran","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-8946","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"},{"name":"Qingdao Key Laboratory of Marine Information Perception and Transmission, 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"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1175\/1520-0477(2000)081<0677:SARNGN>2.3.CO;2","article-title":"SuomiNet: A Real\u2013Time National GPS Network for Atmospheric Research and Education","volume":"81","author":"Ware","year":"2000","journal-title":"Bull. 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