000006477 001__ 6477
000006477 005__ 20230724141528.0
000006477 037__ $$aCTALK-2023-0113
000006477 100__ $$aBamahry, Fikri
000006477 245__ $$aUsing Machine Learning Algorithms for Automated Data Cleaning of GNSS Position Time Series Based on Data Quality Indicators
000006477 260__ $$c2023
000006477 269__ $$c2023-05-26
000006477 520__ $$aData cleaning in GNSS position time series analysis is a critical step that can affect the accuracy and reliability of daily GNSS position and velocity estimates. GNSS time series cleaning methods often involve identifying positions that differ statistically from other positions without knowing the cause of the position outlier. However, data quality degradation is a crucial factor affecting the quality of GNSS position estimates. In this investigation, we implemented a supervised machine-learning algorithm to automatically identify possible position outliers caused by degraded data quality. Our approach investigated the correlation between GPS data quality indicators and outliers in daily position time series to construct a predictive model that can identify possible outliers in daily position time series. Our algorithm was trained using the position time series of EPN stations along with six GPS daily data quality indicators: the number of observed versus expected observations in dual frequency, the lowest elevation cut-off observed, the number of missing epochs, the number of satellites, the number of observations, and the number of cycle slips. Through this process, we identified the most important GPS data quality indicators explaining outliers in the GPS position time series. In this presentation, we will present the preliminary results of our work.
000006477 536__ $$aESFRIFED/$$c EF/$$f211/SERVE / SERVE
000006477 594__ $$aNO
000006477 6531_ $$aGNSS quality metrics
000006477 6531_ $$aGNSS position time series
000006477 6531_ $$aMachine learning
000006477 700__ $$aLegrand, Juliette
000006477 700__ $$aPottiaux, Eric
000006477 700__ $$aBruyninx, Carine
000006477 700__ $$aFabian, Andras
000006477 773__ $$tEUREF Symposium 2023, Gothenburg, Sweden
000006477 8560_ $$ffikri.bamahry@ksb-orb.be
000006477 906__ $$aContributed
000006477 980__ $$aCTALKCONT