## 4 Research Results
### Assessment of the Accuracy of the Gravity Values and height Anomalies Determined from the GGI Model
Calculations were also performed for four versions of the density of the known gravity points: 2.4; 4.9; 9.7; and 19.4 \(\frac{\text{km}^{2}}{1\ point}\). Given that the accuracy of the classical interpolation methods considered here also depends on the density of the topographic masses accepted for reduction [e.g., 9], the calculations were performed for two constant density models: \(\rho_{0}\) = 2200 kg/m\({}^{3}\), \(\rho_{0}\) = 2670 kg/m\({}^{3}\) and for the global topographic mass density model UNB_TopoDens. For comparison, a prediction based on free-air anomalies and Kriging interpolation (\(\Delta g_{FA}\)/Kriging) was also performed. A description of the analyzed prediction versions is given in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Prediction Method**} & \multirow{2}{*}{**Density Model**} & **Value Used to Calculate** & **Prediction Version in** \\  & & **Prediction Accuracy** & **Tables 5 and 6** \\ \hline \multirow{3}{*}{GGI} & \(\rho_{0}\) = 2200 kg/m\({}^{3}\) & \multirow{3}{*}{\(\Delta\text{g}=\text{g}-\text{g}_{GGI}\)} & GGI\({}_{2200}\) \\  & \(\rho_{0}\) = 2670 kg/m\({}^{3}\) & & GGI\({}_{\text{LINB}}\) \\  & \(\rho_{0}\) = UNB\_TopoDens & & \\ \hline \multirow{3}{*}{CBA/Kriging} & \(\rho_{0}\) = 2200 kg/m\({}^{3}\) & \multirow{3}{*}{\(\Delta\text{g}=\text{CBA}_{test}-\text{CBA}_{init}\)} & CBA\({}_{2200}\)/_Kriging_ \\  & \(\rho_{0}\) = 2670 kg/m\({}^{3}\) & & CBA\({}_{2670}\)/_Kriging_ \\  & \(\rho_{0}\) = UNB\_TopoDens & & \\ \hline \multirow{3}{*}{Pratt/Kriging} & \(\rho_{0}\) = 2200 kg/m\({}^{3}\) & \multirow{3}{*}{\(\Delta\text{g}=\Delta g_{Pratt}-\Delta g_{Pratt}\)} & Pratt\({}_{2200}\)/_Kriging_ \\  & \(\rho_{0}\) = 2670 kg/m\({}^{3}\) & & Pratt\({}_{2670}\)/_Kriging_ \\  & \(\rho_{0}\) = UNB\_TopoDens & & Pratt\({}_{LINB}\)/_Kriging_ \\ \hline \(\Delta g_{FA}\)/Kriging & - & \(\Delta\text{g}=\Delta g_{FA}-\Delta g_{FA}\) & FA/_Kriging_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Description of the analyzed versions of the prediction of gravity values. Standard deviations of \(\Delta\)g values in mGal determined for different versions indicated in Table 4 and for different densities of known gravity points for the Slovak part. Standard deviations of the differences indicated in Column 3 of Table 4 were adopted as prediction accuracy parameters for each analyzed method. The standard deviations are shown in Tables 5 and 6. When analyzing the presented results, it should be noted that there is a clear dependence of the prediction accuracy of both: The density of gravity data and the density of topographic masses used in the calculations. The influence of the density of topographic masses is significantly visible for the versions using a lower density of gravity data. However, differences in prediction accuracy are visible for areas of Slovakia (an area with a slightly higher location), even for the highest density of gravity data. The best results for each of the methods were obtained for the version using \(\rho_{0}=2670\) kg/m\({}^{3}\). It is surprising to obtain significantly worse prediction results using the UNB_TopoDens model. This suggests that when predicting gravity in a given area, it is advisable to carry out preliminary tests using various density models and to then choose the density model that gives the best results for the final calculations. By comparing the prediction accuracy of the GGI method and the classical method (using CBA or Pratt anomalies), the results for the highest density of gravity points were obtained at a similar level of accuracy. However, alongside the reduction of the density of gravity data, there is a slight advantage of classical methods at the level of 5 to 10%. It is also worth emphasizing that there is a huge decrease in the accuracy of the prediction if the topography is not included in the prediction process (FA/_Kriging_ version).