## 3 Results  
In the first strategy, training of the artificial neural network was carried out using seven parameters, as shown in Figure 1, as input parameters for each ZWD value. ZWD values were then interpolated to each reference station with this model and a cross validation test was performed. In order to analyze the accuracy, each station was removed from the training data set and ZWD value was interpolated with the ANN model formed by the remaining stations. Afterwards, interpolated ZWD values were compared with ZWD estimated by GNSS observations. In the LIN and IDW methods, the station to be interpolated was removed from the model and the interpolation of ZWD was carried out through the surrounding stations. The same evaluations were performed for 30-station and 20-station networks in order to evaluate the effect of inter-station distance on the interpolation accuracy. The average distance between stations for a 40-station network is around 30-40 km. In the 30-station network, the distance increases to around 50-60 km. The average distance between stations of the 20-station network is around 60-70 km. In the study, correlation coefficients between ZWD values and latitude, longitude, and meteorological parameters were calculated by Equation (15) and are given in Table 1.  
\[k=\frac{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x}\right)\cdot\left(y_{i}- \overline{y}\right)}{\sqrt{\sum\limits_{i=1}^{n}\left(x_{i}-\overline{x} \right)^{2}\cdot\sum\limits_{i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}}} \tag{15}\] \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{1 April} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.29 & 0.54 & \(-\)0.6 & 0.6 & 0.63 & 0.44 & 0.64 & 1 \\  & N & 959 & 959 & 959 & 959 & 959 & 959 & 959 & 959 & 959 \\ \hline \multirow{2}{*}{2 April} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.22 & 0.55 & \(-\)0.88 & 0.89 & 0.88 & 0.62 & 0.88 & 1 \\  & N & 924 & 924 & 924 & 924 & 924 & 924 & 924 & 924 & 924 \\ \hline \multirow{2}{*}{15 July} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.48 & 0.56 & \(-\)0.74 & 0.75 & 0.7 & 0.59 & 0.67 & 1 \\  & N & 953 & 953 & 953 & 953 & 953 & 953 & 953 & 953 \\ \hline \multirow{2}{*}{16 July} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.34 & 0.6 & \(-\)0.83 & 0.83 & 0.81 & 0.64 & 0.78 & 1 \\  & N & 953 & 953 & 953 & 953 & 953 & 953 & 953 & 953 \\ \hline \multirow{2}{*}{15 December} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.49 & 0.4 & \(-\)0.73 & 0.74 & 0.63 & 0.56 & 0.71 & 1 \\  & N & 956 & 956 & 956 & 956 & 956 & 956 & 956 & 956 \\ \hline \multirow{2}{*}{16 December} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.37 & 0.69 & \(-\)0.53 & 0.56 & 0.3 & 0.73 & 0.58 & 1 \\  & N & 953 & 953 & 953 & 953 & 953 & 953 & 953 & 953 \\ \hline \multirow{2}{*}{Mean} & **ZWD** & **Latitude** & **Longitude** & **H** & **P** & **T** & **T\({}_{\mathbf{M}}\)** & **E** & **ZWD** \\  & Correlation & 0.37 & 0.56 & \(-\)0.72 & 0.73 & 0.66 & 0.60 & 0.71 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Correlation coefficients between meteorological parameters and zenith wet delay. It can be seen that station height is negatively correlated with ZWD by 72%. The mean correlations between latitude and longitude and ZWD are 37% and 56%, respectively. The correlations between meteorological parameters and ZWD are found to be highest for P (mean 73%) and E (mean 71%) parameters.