## 4 Validation and Comparison
### Prediction Results Analysis  
In total, we obtained 10,000 RT simulation data and 5065 measurement data, then divided the obtained RT data and measurement data, respectively, into the training dataset and validation dataset in proportions of 80% and 20%. The RT training dataset was utilized for the initial training of the PTM, followed by the measurement training dataset being utilized for the optimization training of the OTM. It should be mentioned that, in order to verify the property that our proposed model can decrease the usage of measurement data while ensuring the prediction accuracy, only 30% of the measurement training dataset was utilized to further optimize the OTM. For the network structure, the input parameters of both PTM and OTM were the propagation distance, UAV height, and carrier frequency, and the output parameter was the PL. Therefore, the number of nodes in the input and output layers were set to 3 and 1, respectively. The number of hidden layers was set to 2, with each layer comprising 10 nodes. The PTM and the OTM had the same network structure in order to ensure the successful transmission of network parameters. The Adam optimizer was employed in the NN to ensure the correct update of the loss function parameters with a suitable magnitude, and the learning rate was configured as 0.001. After training, the prediction results were compared with the remaining 20% validation set. For the LoS case, the prediction results of the proposed model, 3 GPP, and CI model at UAV heights of 10 m and 20 m are shown in Figure 6. We can see that the PLEs of the CI model were fitted to 2.15 at a UAV height of 10 m and 2.26 at a height of 20 m, respectively. The standard deviations of SF were 3.031 dB and 3.279 dB, respectively. The 3 GPP model was obtained through the formula of the LoS case under RMa in [31], where the average \begin{table}
\begin{tabular}{c c c} \hline \hline
**Ground Material** & **Relative Dielectric Constant** & **Conductivity (S/m)** \\ \hline Crops with dry earth & 2.12 & 0.001 \\ Crops with wet earth & 2.2 & 0.018 \\ Paddy fields & 1.85 & 0.18 \\ Dense crops (without exposed earth) & 2.1 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Modified electromagnetic parameters of four ground materials. Figure 5: Four types of sub-scenarios in the farmland area. height of buildings was set to 5 m. For comparison purposes, the RT validation dataset and measurement validation dataset are drawn in the figure as well. As shown in Figure 6, since the PL of the LoS path was not affected by the scatterer materials, the obtained RT simulation data relatively align with the measurement data. The measured PLs were slightly influenced by the elevation angles of the propagation paths because the actual antenna pattern was not perfectly isotropic. The predicted PL of DWN-ANN after pre-training and optimization training were, respectively, consistent with the RT validation dataset and measurement validation dataset, which demonstrates the accuracy of the proposed model. Moreover, the predicted values of our proposed model were consistent with the 3 GPP model and closer to the measurement data than the CI model, which verifies that the proposed model conforms to the 3 GPP standard and is superior to the traditional empirical model, allowing for the accurate prediction of the PL of the LoS path in the farmland scenario. For the NLoS case, the prediction results of the proposed model, 3 GPP, and CI model at UAV heights of 15 m and 30 m are given in Figure 7. Here, default grassland ground  
Figure 6: DWN-ANN, RT validation dataset, measurement validation dataset, 3 GPP, and CI model of LoS case at UAV heights of (**a**) 10 m and (**b**) 20 m.  
material parameters were utilized to acquire RT simulation data without RT pre-correction.