## 1 Introduction
With the use of ANN algorithms based on meteorological variables, developments have been achieved in terms of tropospheric parameter estimation, the temporal prediction of ZWD, weighted mean temperature interpolation, and hourly heavy rainfall estimation [14; 15; 16; 17; 18]. Numerous studies have been conducted with different ANN architectures. The study conducted by [PERSON] [19] focuses on maximum and minimum temperature forecasting and relative humidity prediction using time series analysis based on a Multilayer Perceptron (MLP) feed-forward ANN with back-propagation learning. An evaluation of the ANN model was carried out for a 15 week period and the error was found to be less than 3%. A short-term temperature estimation study using the ANN model was evaluated by [PERSON] [20]. The best performance for temperature estimation was found to be provided by a three-layer MLP network with six hidden neurons. Another artificial neural network study for rainfall forecasting based on MLP architecture was conducted by [PERSON] [21]. The test results showed that a combination of meteorological parameters, such as relative humidity, air pressure, wet bulb temperature, and cloudiness as an input for the ANN model could significantly improve the forecast accuracy and efficiency. In the literature, numerous ANN studies for meteorological predictions have been carried out based on MLP architecture [13; 16; 17; 21; 22]. In addition, different models have been applied for the modeling of atmospheric parameters. Evaluations of an ANN, an adaptive neuro-fuzzy inference system (ANFIS), and gene expression programming (GEP) have been carried out for the monthly modeling of evapotranspiration in the study [23]. Daily weather data, maximum temperature, minimum temperature, relative humidity, wind speed, and sunshine hours were used in the applications. The ANN and ANFIS models provided a better accuracy than the GEP in modeling daily evapotranspiration. In [24], dew point temperature estimation was performed based on hybrid multilayer perceptron neural network models. The wet bulb temperature (WBT), vapor pressure (VP), relative humidity (RH), and dew point temperature were used for an estimation of the dew point temperature. The best performance was obtained by MLP with nature-inspired optimization algorithms compared to support vector machine (SVM) algorithms. In addition, tropospheric interpolation studies conducted by different methods, including linear and non-linear models in GNSS networks, have been performed. In the study conducted by [PERSON] et al. [25], the differences between the zenith troposphere delay (ZTD) calculated by the standard troposphere model of Wide Area Differential Global Satellite Systems (WADGNSS) and the ZTD predicted from the GNSS measurements were used. The residuals were determined on \(1^{0}\times 1^{0}\) grids and the difference value for each grid point was interpolated by the Ordinary Kriging (OK) method. When the results are examined, it can be seen that there is a difference of 2 cm between the values obtained from the surface of the grid and the values obtained from the GNSS observations. [PERSON], [PERSON] et al. [26] tried to eliminate the error of the tropospheric signal delay effect on the coordinate around 1 m in Synthetic Aperture Radar (SAR) applications in their study. For this purpose, the interpolation of residual errors (differences) between the GNSS measurements and the predicted ZTD with the Global Pressure and Temperature model (GPT) and Neutral Atmosphere Delay Model (UNB3m) were calculated. ZTD values were determined in the \(0.5^{0}\)\(\times\)\(0.5^{0}\) grid range by using the OK method and bilinear interpolation was used in the ZTD calculation at the user location. In the study, one-week GNSS measurements of the European GPS Stations (EPN) network were used. In addition, the effect of troposphere delay on the accuracy of the Interferometric Synthetic Aperture Radar (INSAR) technique was reported by [PERSON] et al. [27], [PERSON] et al. [28], and [PERSON] et al. [29].