## 3 Results
The correlation between the wet delay parameter and the height parameter and pressure parameter has almost the same absolute value and these correlation values are inverse to each other. On the other hand, the T parameter has a positive 66% correlation with ZWD. Furthermore, it can be seen that the T\({}_{\text{M}}\) parameter has a mean correlation of 60%. According to these results, it is possible to learn that all meteorological parameters have a significant correlation with ZWD. The T\({}_{\text{M}}\) value is a parameter that cannot be measured directly by the meteorological stations, but it can be measured as close to reality as possible along the thickness of the troposphere with the help of meteorological balloons. In the Bevis model, the empirical equation between the surface temperature and weighted mean temperature parameters is found based on long-term radiosonde measurements. In this study, T\({}_{\text{M}}\) values were calculated by using the Bevis model (Equation (16)) based on T\({}_{0}\) values obtained from TAWES stations. \[\text{T}_{\text{M}}=70.2+0.72^{\bullet}\text{T}_{0} \tag{16}\]  
The water vapor pressure values (E) were calculated using the following equation by employing relative humidity values measured through in-situ observations:  
\[E=\frac{RH}{100}\times E_{s}. \tag{17}\]  
Here, \(E_{s}\) is the correction of saturated water vapor, obtained by Equation (18) and replaced in Equation (17) above. \[E_{s}=\left\{ \begin{array}{l}t\geq 0:0.6105\exp\left(\frac{17.269t}{237.3+t} \right)\\ t<0:0.6105\exp\left(\frac{21.875t}{265.5+t}\right)\end{array} \right\} \tag{18}\]  
The effects of the parameters used in the ANN model on the performance of interpolation are given in Figure 3.  
As shown in the Figure 3, the improvement obtained by including each parameter in the network is shown proportionally for the RMSE values. This proportional improvement can be seen separately in the graph for 40-, 30-, and 20-station networks. It is seen that the height parameter included in the ANN network provides a greater improvement than the other parameters due to it having a strong relationship with the amount of water vapor. The improvement achieved by the pressure (P) parameter is found to be less than that obtained by the elevation parameter. The contribution of the temperature (T) parameter to the success of ANN model interpolation is approximately 14%. Water vapor, which has a linear relationship with temperature, also increases the amount of wet delay in direct proportion. Therefore, the contribution of the temperature parameter included in the ANN network is higher than  
Figure 3: Contribution of meteorological parameters to ANN model accuracy. the weighted mean temperature and water vapor pressure. The water vapor pressure (E) contribute 5.2% to the network. The weighted mean temperature parameter (T\({}_{\text{M}}\)) is seen to provide the least improvement, with a value of 4.4%. The calculation of T\({}_{\text{M}}\) was carried out based on an empirical model, as previously mentioned. Therefore, it is thought that it provides less improvement compared to other parameters. Furthermore, it can be seen that these results are consistent with the correlation values between ZWD and the parameters given in Table 1. The RMSE values of the ANN (from the two-parameter model to the seven-parameter model), LIN, and IDW methods are given in Figure 4.  
According to the RMSE values of the LIN and IDW methods, it can be seen that the conventional methods produce results close to the two-parameter ANN model. The 40-station network interpolation results with a two-parameter ANN model have an RMSE of 8.8 mm, while the LIN method has an RMSE of 11.6 mm and the IDW method has an RMSE of 11.5 mm. The results of interpolation obtained by the ANN model demonstrate that as the number of parameters increases, the RMSE values decrease.