## 4 Validation and Comparison
### Prediction Results Analysis
The 3 GPP model was obtained through the formula of NLoS case under RMa in [31], where the average height of buildings was set to 5 m, the average width of streets was set to 4 m, the antenna height of the user terminal was set to 2.5 m, and the antenna height of the base station was set according to the different UAV heights. The PLEs of the CI model at UAV heights of 15 m and 30 m were, respectively, fitted to 2.79 and 2.88, which are slightly smaller than the PLE in urban scenarios. The standard deviations of SF were 3.652 dB and 4.112 dB, respectively. Due to the complexity of the ground materials and the significant influence of ground materials on the ground reflection path, without RT pre-correction, there was a relatively significant difference between the obtained RT simulation data and the measurement data. It was found that the prediction results of DWN-ANN after pre-training still matched the RT validation dataset, but the corresponding results after optimization training were slightly lower than the measurement data, which demonstrates a decreased prediction  
Figure 7: DWN-ANN (without RT pre-correction), RT validation dataset, measurement validation dataset, 3 GPP, and CI model of NLoS case at UAV heights of (**a**) 15 m and (**b**) 30 m.  
accuracy in comparison to the LoS case. However, the predicted PL was still close to the 3 GPP and CI model, indicating the validity of the proposed model. Even when using default RT material parameters and with limited measurement data, the DWN-ANN can still improve prediction accuracy through joint training with RT and measurement data, achieving similar prediction results to traditional models. After RT pre-correction, the corresponding prediction values are illustrated in Figure 8. As depicted in the figure, after correcting ground material parameters of the farmland using measurement data, the obtained RT simulation data of NLoS path were in better agreement with the measurement data. The predicted PL of DWN-ANN after pre-training and optimization training, respectively, aligned with the RT validation dataset and measurement validation dataset, and were closer to the measurement data than 3 GPP and CI model, which indicates the superiority of our proposed model in the NLoS case, which can accurately predict the PL of the NLoS path in the farmland scenario. Furthermore, it also proves the necessity of the RT pre-correction module in scenarios with complex materials. Figure 8: DWN-ANN (with RT pre-correction), RT validation dataset, measurement validation dataset, 3 GPP, and CI model of NLoS case at UAV heights of (**a**) 15 m and (**b**) 30 m.  
In order to intuitively demonstrate the prediction accuracy of different models, the root mean square error (RMSE) between the measurement data and the CI model, 3 GPP model, and the proposed DWN-ANN model is shown in Table 3. According to the above analysis, it can be concluded that the ground materials of farmland scenarios significantly impact the RT simulation accuracy. Our proposed model can achieve similar prediction accuracy to traditional CI and 3 GPP models when using default ground materials in RT simulation. After the pre-correction of the ground material parameters, the prediction values are consistent with the measurement data and superior to traditional models. In summary, the proposed model can achieve a favorable equilibrium between prediction accuracy and the required amount of measurement data through joint training with RT and measurement data. Moreover, it can enhance prediction accuracy via RT pre-correction, achieving accurate PL prediction under LoS and NLoS paths in farmland scenarios. The proposed model solves the issue of PL prediction in the case of insufficient measurement data and inaccurate RT simulation in the farmland scenario.