Using the observations from local and regional GPS networks, the estimation of slant wet delays (SWDs) is possible for each line of sight between satellite and receiver. The observations of SWD are used to model horizontal and vertical variations of the wet refractivity in the atmosphere above the study area. This work is done using the tomography method. In tomography, the horizontal variations of tropospheric wet refractivity are modeled with the polynomial in degree and rank of 2 with latitude and longitude as variables. Also, altitude variations are modeled in the form of discrete layers with constant heights. The main innovation is to estimate the tropospheric parameters for each line of sight by the artificial neural networks (ANNs). The SWD obtained from GPS observations for the different signals at each station is compared with the SWD generated by the ANNs (SWD_GPS–SWD_ANNs). The square of the difference between these two values is introduced as the cost function in the ANNs. To evaluate, we used observations from October 27 to 31, 2011. The availability of GPS and radiosonde data is the main reason for choosing this timeframe. The correlation coefficient, root mean square error (RMSE), and relative error allow for evaluation of the proposed model. The results were also compared with the results of the voxel-based troposphere tomography method. For a more detailed evaluation, four test stations are selected and ANN zenith wet delays (ZWD_ANN) are compared with the ZWD_GPS. Observations of test stations are not used in the modeling step. The correlation coefficient in the testing step for Tomo_ANN and Tomo_voxel is 0.9006 and 0.8863, respectively. The mean RMSE at 5 days for Tomo_ANN and Tomo_voxel is calculated as 0.63 and 0.71 mm/km, respectively. Also, the average relative error at the four test stations for Tomo_ANN is 15.37% and for Tomo_voxel it is 19.69%. The results demonstrate the better capability of the proposed method in the modeling of the tropospheric wet refractivity in the region of Iran.

中文翻译：

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用层析成像方法和人工神经网络估算对流层湿折射率的伊朗案例研究

使用来自本地和区域GPS网络的观测结果，可以估算卫星和接收器之间每条视线的倾斜湿延迟（SWD）。SWD的观测值用于模拟研究区域上方大气中湿折射率的水平和垂直变化。这项工作是使用断层扫描方法完成的。在层析成像中，对流层湿折射率的水平变化是用2的次数和等级的多项式建模的，其中纬度和经度为变量。同样，高度变化以具有恒定高度的离散层的形式建模。主要创新是通过人工神经网络（ANN）估算每个视线的对流层参数。_GPS –SWD _ANN）。这两个值之差的平方被引入为人工神经网络中的成本函数。为了进行评估，我们使用了2011年10月27日至31日的观测数据。GPS和无线电探空仪数据的可用性是选择此时间范围的主要原因。相关系数，均方根误差（RMSE）和相对误差允许对提出的模型进行评估。还将结果与基于体素的对流层层析成像方法的结果进行了比较。为了进行更详细的评估，选择了四个测试站，并将ANN天顶湿延迟（ZWD _ANN）与ZWD _GPS进行了比较。在建模步骤中不使用测试站的观测结果。Tomo测试步骤中的相关系数_ANN和Tomo_体素分别为0.9006和0.8863。Tomo _ANN和Tomo_体素在5天时的平均RMSE分别计算为0.63和0.71 mm / km。同样，四个测试站的Tomo _ANN的平均相对误差为15.37％，Tomo_体素的平均相对误差为19.69％。结果证明了该方法在伊朗地区对流层湿折射率建模中具有更好的能力。