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    Quality control of the GNSS-IR sea level measurements by using K-means clustering
    (Taylor & Francis Ltd, 2025) Hatipoglu, Cansu Besel; Kayikci, Emine Tanir
    Quality control is a crucial step in GNSS-IR data processing and is performed in this study using two methods: the peak-to-noise ratio and K-means clustering. Both quality control methods are applied to the SNR data at the MERS, TRBZ, and SNOP sites. K-means clustering shows better performance for the MERS GPS L1, Galileo L1, and SNOP GPS L2, while the peak-to-noise ratio shows better performance for the TRBZ GPS L1. The correlation coefficient between the GNSS-IR sea levels from the L1 signal and tide gauge is greater than 85%. These results demonstrate that K-means clustering is promising for quality control.