On performance assessment of machine learning- based GNSS ionospheric delay correction model based on space weather predictors in immediate positioning environment (CROSBI ID 729492)
Prilog sa skupa u zborniku | sažetak izlaganja sa skupa | međunarodna recenzija
Podaci o odgovornosti
Filjar, Renato
engleski
On performance assessment of machine learning- based GNSS ionospheric delay correction model based on space weather predictors in immediate positioning environment
Availability of advanced open-source statistical computing environments, such as R and Python as well as GUI-based derivatives of those, combined with the abundance of space weather observations openyl available attract a growing number of researchers to develop predictive and explanatory machine learning-based models. Experience show the insufficient attention is given to model performance assessment, and selection of feasible models based on their performance. Here we argue the more consideration should be given to machine learning-based model performance assessment, propose the statistics-based methodology for space weather-regarded model performance assessment, and demonstrate its deployment in the case of machine learning-based GNSS ionospheric correction model based on space weather predictors in the immediate positioning environment of a GNSS receiver.
GNSS ; ionospheric correction model ; machine learning ; performance assessment
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Podaci o prilogu
21
2022.
objavljeno
Podaci o matičnoj publikaciji
Gadimova, Sharafat ; Groves, Keith ; Orué, Yenca Migoya ; Molina, María Graciela ; Nava, Bruno
Buenos Aires, Argentina, and Trieste, Italy: ICTP
Podaci o skupu
International Workshop on Machine Learning for Space Weather: Fundamentals, Tools and Future Prospects
pozvano predavanje
07.11.2022-11.11.2022
Buenos Aires, Argentina
Povezanost rada
Elektrotehnika, Matematika, Računarstvo, Zrakoplovstvo, raketna i svemirska tehnika