## 2 Materials and Methods  
### Preparation of the Dataset  
The dataset to be used in machine learning applications is of great importance for the success and accuracy of the model. Having a dataset that has been tested and approved in different studies in the literature saves time before the application. However, not having a ready dataset can complicate the pre-application process, but it also provides in-depth information during the data collection and preparation phase. Creating our own dataset for the application to be made using ML methods included collecting the specific data needed, cleaning these data, and labeling them appropriately. These stages are very important for obtaining accurate and reliable results, but they can also be time-consuming. The quality of the data directly affects the performance of the machine learning model; therefore, creating a good dataset by allocating enough time and resources is important for obtaining a more successful model in the long run.  
In the literature, there is no ready dataset that includes the location, velocity accuracies, and year intervals of GNSS stations that can be used with machine learning algorithms. Therefore, the development of the machine learning (ML) model required the creation of a new dataset. To create this data set, four GNSS stations (BALK, BILE, ESKS, and HARC) belonging to the Turkish National Permanent GNSS Network Active (TUSAGA-Active, also known as CORS-TR) network in Turkey were selected considering their time series and whether each station had data on the same day of the years 2011-2017 (Figures 1 and 2). During this period, no significant earthquakes occurred near the stations that would have caused any translational movement. The 24-h GNSS data from the selected stations were downloaded from the TUSAGA-Active website and were divided into 2-, 4-, 6-, 8-, and 12-h periods with Translation, Editing, and Quality Check (TEQC) software (February 2019 version). The precise Earth Orientation Parameters (EOPs), satellite orbits, and clocks [25] needed for the process were obtained from the IERS Bulletin EOP [26], while the antenna phase center models were obtained from the International GNSS Service (IGS). Noise stochasticity and offsets can significantly impact the accuracy of GNSS-derived velocity time series [27]. However, the GAMIT/GLOBK v10.71 software, employed for GNSS data processing, utilizes stochastic modeling, Kalman filtering, and statistical analysis of residuals to enhance the accuracy and reliability of velocity estimates [28]. Detailed information on this issue can be found in [28].  
Figure 1: Locations of the stations.  
Figure 2: Time series of ESKS station.  
Following the processing of the GNSS data, a total of 1500 velocities were calculated in the Eurasian-fixed ITRF14 reference frame, with 375 velocities per station across four stations, to evaluate the effect of positional accuracy on velocity accuracy for each campaign. These velocities were calculated by considering all possible combinations of position data derived from GNSS observations conducted for 2, 4, 6, 8, and 12 h in each campaign, ensuring that only one observation duration was selected from each year. Following the processing of GNSS data and the velocity generation stage, the relevant data from the files, including the position and velocity accuracies produced by the GAMIT/GLOBK software [28], were extracted by writing small Python scripts, which were saved as a Comma Separated Values (CSV) file. In the dataset, there are seven independent variables (year interval, Se1, Sn1, Se2, Sn2, Se3, Sn3) and two dependent variables (Sve, Svn). Afterwards, velocity accuracies and position accuracies were separated on the basis of components, and two separate datasets were created. Detailed information about the datasets is given in Tables 1 and 2.