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A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network / Wang Li in Advances in space research, vol 67 n° 1 (January 2021)  

Public [article]  inAdvances in space research > vol 67 n° 1 (January 2021) . - pp 20 - 34 Titre : A new method for improving the performance of an ionospheric model developed by multi-instrument measurements based on artificial neural network Type de document : Article/Communication Auteurs : Wang Li, Auteur ; Changyong He , Auteur ; Andong Hu, Auteur ; Dongsheng Zhao, Auteur ; Yi Shen, Auteur ; Kefei Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 20 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] correction ionosphérique [Termes IGN] image Formosat/COSMIC [Termes IGN] modèle ionosphérique [Termes IGN] réseau neuronal artificiel [Termes IGN] teneur totale en électrons Résumé : (auteur) There are remarkable ionospheric discrepancies between space-borne (COSMIC) measurements and ground-based (ionosonde) observations, the discrepancies could decrease the accuracies of the ionospheric model developed by multi-source data seriously. To reduce the discrepancies between two observational systems, the peak frequency (foF2) and peak height (hmF2) derived from the COSMIC and ionosonde data are used to develop the ionospheric models by an artificial neural network (ANN) method, respectively. The averaged root-mean-square errors (RMSEs) of COSPF (COSMIC peak frequency model), COSPH (COSMIC peak height model), IONOPF (Ionosonde peak frequency model) and IONOPH (Ionosonde peak height model) are 0.58 MHz, 19.59 km, 0.92 MHz and 23.40 km, respectively. The results indicate that the discrepancies between these models are dependent on universal time, geographic latitude and seasons. The peak frequencies measured by COSMIC are generally larger than ionosonde’s observations in the nighttime or middle-latitudes with the amplitude of lower than 25%, while the averaged peak height derived from COSMIC is smaller than ionosonde’s data in the polar regions. The differences between ANN-based maps and references show that the discrepancies between two ionospheric detecting techniques are proportional to the intensity of solar radiation. Besides, a new method based on the ANN technique is proposed to reduce the discrepancies for improving ionospheric models developed by multiple measurements, the results indicate that the RMSEs of ANN models optimized by the method are 14–25% lower than the models without the application of the method. Furthermore, the ionospheric model built by the multiple measurements with the application of the method is more powerful in capturing the ionospheric dynamic physics features, such as equatorial ionization, Weddell Sea, mid-latitude summer nighttime and winter anomalies. In conclusion, the new method is significant in improving the accuracy and physical characteristics of an ionospheric model based on multi-source observations. Numéro de notice : A2021-986 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.asr.2020.07.032 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1016/j.asr.2020.07.032 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102912 [article]

Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations / Wang Li in Remote sensing, vol 12 n° 5 (March 2020)  

Public [article]  inRemote sensing > vol 12 n° 5 (March 2020) . - n° 866 Titre : Advanced machine learning optimized by the genetic algorithm in ionospheric models using long-term multi-instrument observations Type de document : Article/Communication Auteurs : Wang Li, Auteur ; Dongsheng Zhao, Auteur ; Changyong He , Auteur ; Andong Hu, Auteur ; Kefei Zhang, Auteur Année de publication : 2020 Projets : 3-projet - voir note / Article en page(s) : n° 866 Note générale : bibliographie This research was funded by the National Natural Science Foundations of China, grant number 41730109, the Priority Academic Program Development of Jiangsu Higher Education Institutions (Surveying and Mapping) and the Jiangsu Dual Creative Talents and Jiangsu Dual Creative Teams Programme Projects awarded in 2017. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie spatiale [Termes IGN] algorithme génétique [Termes IGN] image Formosat/COSMIC [Termes IGN] International Reference Ionosphere [Termes IGN] modèle ionosphérique [Termes IGN] réseau neuronal artificiel [Termes IGN] teneur totale en électrons Résumé : (auteur) The ionospheric delay is of paramount importance to radio communication, satellite navigation and positioning. It is necessary to predict high-accuracy ionospheric peak parameters for single frequency receivers. In this study, the state-of-the-art artificial neural network (ANN) technique optimized by the genetic algorithm is used to develop global ionospheric models for predicting foF2 and hmF2. The models are based on long-term multiple measurements including ionospheric peak frequency model (GIPFM) and global ionospheric peak height model (GIPHM). Predictions of the GIPFM and GIPHM are compared with the International Reference Ionosphere (IRI) model in 2009 and 2013 respectively. This comparison shows that the root-mean-square errors (RMSEs) of GIPFM are 0.82 MHz and 0.71 MHz in 2013 and 2009, respectively. This result is about 20%–35% lower than that of IRI. Additionally, the corresponding hmF2 median errors of GIPHM are 20% to 30% smaller than that of IRI. Furthermore, the ANN models present a good capability to capture the global or regional ionospheric spatial-temporal characteristics, e.g., the equatorial ionization anomaly and Weddell Sea anomaly. The study shows that the ANN-based model has a better agreement to reference value than the IRI model, not only along the Greenwich meridian, but also on a global scale. The approach proposed in this study has the potential to be a new three-dimensional electron density model combined with the inclusion of the upcoming Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC-2) data. Numéro de notice : A2020-872 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12050866 Date de publication en ligne : 07/03/2020 En ligne : https://doi.org/10.3390/rs12050866 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99659 [article]