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Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions.<\/jats:p>","DOI":"10.3390\/rs14102433","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:18:11Z","timestamp":1653005891000},"page":"2433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":106,"title":["An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1292-6746","authenticated-orcid":false,"given":"Jun","family":"Tang","sequence":"first","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"},{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"},{"name":"Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0715-3574","authenticated-orcid":false,"given":"Yinjian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2256-9663","authenticated-orcid":false,"given":"Mingfei","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6167-408X","authenticated-orcid":false,"given":"Dengpan","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Transportation Engineering, East China Jiaotong University, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuequn","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2021SW002872","DOI":"10.1029\/2021SW002872","article-title":"E-CHAIM as a model of total electron content: Performance and diagnostics","volume":"19","author":"Themens","year":"2021","journal-title":"Space Weather"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"12839","DOI":"10.1029\/2000JA900005","article-title":"Nonlinear prediction of the ionospheric parameter foF2 on hourly, daily, and monthly timescales","volume":"105","author":"Francis","year":"2000","journal-title":"J. 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