Naslov
On performance assessment of machine learning- based GNSS ionospheric delay correction model based on space weather predictors in immediate positioning environment

Autori
Filjar, Renato

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, pp prezentacija, znanstveni

Izvornik
Proc of ICTP International Workshop on Machine Learning for Space Weather: Fundamentals, Tools and Future Prospects (on-line proceedings) / Gadimova, Sharafat ; Groves, Keith ; Orué, Yenca Migoya ; Molina, María Graciela ; Nava, Bruno - Buenos Aires, Argentina, and Trieste, Italy : ICTP, 2022

Skup
International Workshop on Machine Learning for Space Weather: Fundamentals, Tools and Future Prospects

Mjesto i datum
Buenos Aires, Argentina, 07.11.2022. - 11.11.2022

Vrsta sudjelovanja
Pozvano predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
GNSS ; ionospheric correction model ; machine learning ; performance assessment

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Elektrotehnika, Računarstvo, Zrakoplovstvo, raketna i svemirska tehnika

POVEZANOST RADA