{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T21:40:14Z","timestamp":1723412414095},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T00:00:00Z","timestamp":1672358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangxi Science and Technology Base and Talent Project","award":["Guike AD19245060"]},{"name":"Guangxi Natural Science Foundation of China","award":["2020GXNSFBA159033"]},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics","award":["19-050-11-24"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42064002"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"The ionospheric total electron content (TEC) is susceptible to factors, such as solar and geomagnetic activities, resulting in the enhancement of its non-stationarity and nonlinear characteristics, which aggravate the impact on radio communications. In this study, based on the NeuralProphet hybrid prediction framework, a regional ionospheric TEC prediction model (multi-factor NeuralProphet model, MF-NPM) considering multiple factors was constructed by taking solar activity index, geomagnetic activity index, geographic coordinates, and IGS GIM data as input parameters. Data from 2009 to 2013 were used to train the model to achieve forecasts of regional ionospheric TEC at different latitudes during the solar maximum phase (2014) and geomagnetic storms by sliding 1 day. In order to verify the prediction performance of the MF-NPM, the multi-factor long short-term memory neural network (LSTMNN) model was also constructed for comparative analysis. At the same time, the TEC prediction results of the two models were compared with the IGS GIM and CODE 1-day predicted GIM products (COPG_P1). The results show that the MF-NPM achieves good prediction performance effectively. The RMSE and relative accuracy (RA) of MF-NPM are 2.33 TECU and 93.75%, respectively, which are 0.77 and 1.87 TECU and 1.91% and 6.68% better than LSTMNN and COPG_P1 in the solar maximum phase (2014). During the geomagnetic storm, the RMSE and RA of TEC prediction results based on the MF-NPM are 3.12 TECU and 92.86%, respectively, which are improved by 1.25 and 2.30 TECU and 2.38% and 7.24% compared with LSTMNN and COPG_P1. Furthermore, the MF-NPM also achieves better performance in low\u2013mid latitudes.<\/jats:p>","DOI":"10.3390\/rs15010195","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T08:18:18Z","timestamp":1672388298000},"page":"195","source":"Crossref","is-referenced-by-count":2,"title":["Spatiotemporal Analysis of Regional Ionospheric TEC Prediction Using Multi-Factor NeuralProphet Model under Disturbed Conditions"],"prefix":"10.3390","volume":"15","author":[{"given":"Ling","family":"Huang","sequence":"first","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8326-0216","authenticated-orcid":false,"given":"Han","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2675-5677","authenticated-orcid":false,"given":"Yidong","family":"Lou","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Hongping","family":"Zhang","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Lilong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4241-3730","authenticated-orcid":false,"given":"Liangke","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China"},{"name":"Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin 541004, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2022RG000792","DOI":"10.1029\/2022RG000792","article-title":"The International Reference Ionosphere Model: A Review and Description of an Ionospheric Benchmark","volume":"60","author":"Bilitza","year":"2022","journal-title":"Rev. Geophys."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TAES.1987.310829","article-title":"Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users","volume":"AES-23","author":"Klobuchar","year":"1987","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"unstructured":"Bent, R.B., Llewellyn, S.K., Nesterczuk, G., and Schmid, P. (1975). The development of a highly-successful worldwide empirical ionospheric model and its use in certain aspects of space communications and worldwide total electron content investigations. Effect of the Ionosphere on Space Systems and Communications, National Technical Information Service.","key":"ref_3"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1016\/j.jastp.2008.01.015","article-title":"A new version of the NeQuick ionosphere electron density model","volume":"70","author":"Nava","year":"2008","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s11600-020-00515-z","article-title":"Broadcast ionospheric delay correction algorithm using reduced order adjusted spherical harmonics function for single-frequency GNSS receivers","volume":"69","author":"Abhigna","year":"2021","journal-title":"Acta Geophys."},{"unstructured":"Georgiadiou, Y. (1994). Modeling the Ionosphere for an Active Control Network of GPS Stations, Delft Geodetic Computing Centre.","key":"ref_6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"59","DOI":"10.11003\/JKGS.2013.2.1.059","article-title":"Performance evaluation of ionosphere modeling using spherical harmonics in the Korean Peninsula","volume":"2","author":"Han","year":"2013","journal-title":"J. Position. Navig. Timing"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1659","DOI":"10.1007\/s00190-019-01275-5","article-title":"Regional ionospheric TEC modeling based on a two-layer spherical harmonic approximation for real-time single-frequency PPP","volume":"93","author":"Li","year":"2019","journal-title":"J. Geod."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"GD102","DOI":"10.4401\/ag-8433","article-title":"Total Electron Content (TEC) estimation over Pakistan from local GPS network using spherical harmonics","volume":"64","author":"Mehmood","year":"2021","journal-title":"Ann. Geophys."},{"unstructured":"Schaer, S. (1999). Mapping and predicting the Earth\u2019s ionosphere using the Global Positioning System. [Ph.D. Thesis, University of Bern].","key":"ref_10"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.actaastro.2020.08.034","article-title":"Modeling and analysis of ionospheric TEC variability from GPS\u2013TEC measurements using SSA model during 24th solar cycle","volume":"178","author":"Dabbakuti","year":"2021","journal-title":"Acta Astronaut."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.asr.2017.01.031","article-title":"Performance evaluation of ionospheric time delay forecasting models using GPS observations at a low-latitude station","volume":"60","author":"Sivavaraprasad","year":"2017","journal-title":"Adv. Space Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2848","DOI":"10.1016\/j.asr.2018.03.024","article-title":"Development of multivariate ionospheric TEC forecasting algorithm using linear time series model and ARMA over low-latitude GNSS station","volume":"63","author":"Ratnam","year":"2019","journal-title":"Adv. Space Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1029\/97RS00431","article-title":"Neural network modeling of the ionospheric electron content at global scale using GPS data","volume":"32","author":"Juan","year":"1997","journal-title":"Radio Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"410","DOI":"10.2478\/s11600-007-0011-9","article-title":"Spatial correlation of foF2 and vTEC under quiet and disturbed ionospheric conditions: A case study","volume":"55","author":"Cander","year":"2007","journal-title":"Acta Geophys."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.jastp.2010.01.012","article-title":"TEC measurements and modelling over Southern Africa during magnetic storms; a comparative analysis","volume":"72","author":"Habarulema","year":"2010","journal-title":"J. Atmos. Sol. Terr. Phys."},{"doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, J., Yang, C., Zheng, Y., and Fu, H. (2022). A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map. Remote Sens., 14.","key":"ref_17","DOI":"10.3390\/rs14215579"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1002\/2013RS005247","article-title":"Ionospheric single-station TEC short-term forecast using RBF neural network","volume":"49","author":"Huang","year":"2014","journal-title":"Radio Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.jastp.2016.09.005","article-title":"Wavelet neural networks using particle swarm optimization training in modeling regional ionospheric total electron content","volume":"149","author":"Voosoghi","year":"2016","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2035","DOI":"10.1016\/j.asr.2022.06.020","article-title":"Modeling and forecasting of ionosphere TEC using least squares SVM in central Europe","volume":"70","author":"Moradi","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.asr.2022.04.066","article-title":"Long Short-Term Memory and Gated Recurrent Neural Networks to Predict the Ionospheric Vertical total electron Content","volume":"70","author":"Iluore","year":"2022","journal-title":"Adv. Space Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e2022SW003103","DOI":"10.1029\/2022SW003103","article-title":"An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short-Term Memory Method","volume":"20","author":"Shi","year":"2022","journal-title":"Space Weather"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"e2020SW002706","DOI":"10.1029\/2020SW002706","article-title":"Long Short-Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China","volume":"19","author":"Xiong","year":"2021","journal-title":"Space Weather"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1180","DOI":"10.1109\/LGRS.2019.2895112","article-title":"A Deep Learning-Based Approach to Forecast Ionospheric Delays for GPS Signals","volume":"16","author":"Srivani","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"doi-asserted-by":"crossref","unstructured":"Lin, X., Wang, H., Zhang, Q., Yao, C., Chen, C., Cheng, L., and Li, Z. (2022). A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting. Remote Sens., 14.","key":"ref_25","DOI":"10.3390\/rs14071717"},{"doi-asserted-by":"crossref","unstructured":"Bi, C., Ren, P., Yin, T., Xiang, Z., and Zhang, Y. (2022). Modeling and Forecasting Ionospheric foF2 Variation in the Low Latitude Region during Low and High Solar Activity Years. Remote Sens., 14.","key":"ref_26","DOI":"10.3390\/rs14215418"},{"doi-asserted-by":"crossref","unstructured":"Benoit, A.G., and Petry, A. (2021). Evaluation of F10.7, Sunspot Number and Photon Flux Data for Ionosphere TEC Modeling and Prediction Using Machine Learning Techniques. Atmosphere, 12.","key":"ref_27","DOI":"10.3390\/atmos12091202"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s40328-021-00371-3","article-title":"Comparisons of autoregressive integrated moving average (ARIMA) and long short term memory (LSTM) network models for ionospheric anomalies detection: A study on Haiti (Mw\u2009=\u20097.0) earthquake","volume":"57","author":"Saqib","year":"2022","journal-title":"Acta Geod. Et Geophys."},{"unstructured":"Triebe, O., Hewamalage, H., Pilyugina, P., Laptev, N., Bergmeir, C., and Rajagopal, R. (2021). NeuralProphet: Explainable Forecasting at Scale. arXiv.","key":"ref_29"},{"doi-asserted-by":"crossref","unstructured":"ChikkaKrishna, N.K., Rachakonda, P., and Tallam, T. (2022, January 11\u201313). Short-Term Traffic Prediction Using Fb-PROPHET and Neural-PROPHET. Proceedings of the 2022 IEEE Delhi Section Conference (DELCON), New Delhi, India.","key":"ref_30","DOI":"10.1109\/DELCON54057.2022.9753459"},{"doi-asserted-by":"crossref","unstructured":"Zhang, Y., Hou, J., and Huang, C. (2022). Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China. Remote Sens., 14.","key":"ref_31","DOI":"10.3390\/rs14215355"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/195\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,11]],"date-time":"2024-08-11T21:24:10Z","timestamp":1723411450000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/195"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,30]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010195"],"URL":"https:\/\/doi.org\/10.3390\/rs15010195","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,12,30]]}}}