## 1 Introduction
Considering the need for GNSS stations for modeling tectonic movements, it is not possible to install, operate, and manage dozens of stations for each fault. Instead, periodic campaign-type GNSS measurements are preferred. In this way, larger areas can be modeled tectonically with less cost and effort thanks to GNSS measurements performed once or several times a year. The determination of GNSS velocity has been investigated in various studies [9; 10; 13; 14; 15; 16; 17]. A velocity field obtained from GNSS observations offers essential insights into earthquake hazards, including the moment accumulation rate and strain rate [18]. Velocity data have been extensively utilized by numerous researchers in studies related to crustal motion, plate boundary dynamics, seismic site analysis, and deformation kinematics [3; 4; 5; 9; 10; 19; 20]. Ref. [11] analyzed two distinct Artificial Neural Network (ANN) models, namely, the Back-Propagation Artificial Neural Network and the Radial Basis Function Neural Network, to estimate the velocities of 125 GPS sites in Turkey, and they revealed that the Back-Propagation Artificial Neural Network is an alternative tool to conventional methods for geodetic station velocity estimation. Ref. [21] conducted a comparative assessment of three distinct Artificial Neural Network (ANN) models to estimate the geodetic velocities of 238 sites in Turkey, identifying the generalized regression neural network as the most suitable model. Ref. [22] investigated the applicability of four different deep learning (DL) methods for estimating GPS-derived geodetic velocities at 42 GNSS stations in northwestern Iran. The findings indicate that the CNN method exhibits a lower goodness of fit and higher root mean square error (RMSE). In the most recent study conducted by [17], ML algorithms were used to estimate horizontal GNSS velocities in active tectonic regions. In the study where cluster analysis was used, a velocity accuracy lower than 0.4 mm/year was estimated. Machine learning constitutes a sub-branch of artificial intelligence that processes raw data by using mathematical and statistical methods and gains learning skills by detecting the relationship between data. With the success of machine learning techniques in revealing hidden patterns and complex relationships between data, they have been successfully applied in various fields such as pattern recognition, computer vision, and finance. The purpose of this discipline is to process large amounts of raw data and to create models that enable predictions to be made from this data. Machine learning techniques are grouped under three main headings: supervised learning, unsupervised learning, and reinforcement learning [23,24]. In this study, the GNSS data from four different CORS stations between 2011 and 2017 were divided into different measurement times and evaluated with GAMIT/GLOBK, the position accuracies of each campaign were obtained, and the velocity was produced by creating 375 combinations for each station. The results for the four stations (1500 rows of data) were evaluated with four different supervised machine learning estimation algorithms--(Support Vector Machine (SVM), Multiple Linear Regression (MLR), Random Forest (RF), and K-Nearest Neighbor (KNN))--and the effect of the time between campaigns and position accuracy in each campaign on velocity accuracy was modeled. In this way, we aimed to estimate the velocity accuracy by considering the accuracy achieved since the first campaign in studies where GNSS velocities were used.