{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T00:37:57Z","timestamp":1705106277617},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684802","type":"print"},{"value":"9781643684819","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T00:00:00Z","timestamp":1705017600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,1,12]]},"abstract":"<jats:p>When satellite signals pass through the ionosphere, the total electron content (TEC) is the main physical quantity that causes errors in the ionosphere, Moreover, the short-term prediction of TEC is an important research object in the field of ionospheric monitoring, and the accurate calculation and short-term prediction of ionospheric TEC is one of the most important elements of space weather monitoring and early warning. In this paper, a new crow search algorithm (CSA) is proposed to improve the LSTM neural network by optimizing the initial weights and parameters of long and short-term memory neural network, and to forecast the TEC values in the future period. The experimental results show that in the prediction of the ionosphere at single station for 48 h in 2023 in the Chinese region, the forecast effect at mid-latitudes; the CSA-LSTM forecast model is 0.72 TECu smaller than the average root-mean-square error of the single LSTM forecast model, while the forecast effect at low latitudes; the CSA-LSTM forecast model is smaller than the average root-mean-square error of the single LSTM forecast model by 2.67 TECu, overall, the CSA-LSTM forecast model has higher forecast accuracy and better forecasts at lower latitudes.<\/jats:p>","DOI":"10.3233\/faia231247","type":"book-chapter","created":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:58Z","timestamp":1705064218000},"source":"Crossref","is-referenced-by-count":0,"title":["Short-Term Forecast of Ionospheric TEC Based on CSA-LSTM"],"prefix":"10.3233","author":[{"given":"Tong","family":"Zhu","sequence":"first","affiliation":[{"name":"Beijing Information Science & Technology University, Beijing 100192, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xialan","family":"Chen","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Zhibo","family":"Fang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Electronics, Communications and Networks"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231247","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,12]],"date-time":"2024-01-12T12:56:59Z","timestamp":1705064219000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231247"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,12]]},"ISBN":["9781643684802","9781643684819"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231247","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,12]]}}}