## 4 Discussion  
### Ablation Study  
To evaluate the contribution of each structural component of GPTransCNN to overall performance, this study conducted ablation experiments. By systematically removing or modifying individual model components, the impact of each part on the model's predictive performance was quantitatively assessed, thereby providing a scientific basis for model optimization and mechanistic interpretation [54]. Given that GPTransCNN adopts a dual-branch neural network architecture composed of a GPTransformer branch and a 1D-CNN branch, structural modifications and combinations were designed separately for each branch. The GPTransformer branch consists of three structural variants: Transformer (with original positional encoding), NPTransformer (without positional encoding), and GPTransformer (with geographic positional encoding). Transformer serves as the baseline model, while NPTransformer is designed to assess the impact of removing positional encoding on model performance. GPTransformer modifies the positional encoding mechanism of Transformer by incorporating geographic location information to enhance the model's sensitivity to spatial features. The 1D-CNN branch adopts a fixed architecture composed of a one-dimensional convolutional neural network with four consecutive convolutional blocks. Based on the combinations of the aforementioned GPTransformer and 1D-CNN branches, three dual-branch comparative models were constructed: TransCNN (comprising Transformer and 1D-CNN), NPTransCNN (comprising NPTransformer and 1D-CNN), and GPTransCNN (comprising GPTransformer and 1D-CNN), with the latter being the model proposed in this study. In total, seven models were evaluated for comparative validation. As shown in Table 7, GPTransCNN demonstrated the best performance, with an R\({}^{2}\) improvement of 43.39% over Transformer, 44.49% over NPTransformer, 18.83% over GPTransformer, 14.98% over 1D-CNN, 7.26% over TransCNN, and 7.29% over NPTransCNN. To ensure the robustness and reliability of the experimental results, this study conducted ten-fold cross-validation. AS presented in Table 8, GPTransCNN consistently achieved the highest predictive performance. The results of the comparison reveal that Transformer and NPTransformer exhibit similar performance, indicating that the original positional encoding offers limited effectiveness in the geospatial context of this study. This finding is further corroborated by the comparable outcomes observed between TransCNN and NPTransCNN. In contrast, GPTransformer, which incorporates geographic positional encoding, achieves higher model fitting accuracy, suggesting that embedding geographic information enhances the model's capacity to interpret the spatial heterogeneity of environmental covariates. Furthermore, models \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline Transformer & 0.3019 & 6.8830 & 5.0743 \\ NPTransformer & 0.2996 & 6.8946 & 5.1991 \\ GPTransformer & 0.3643 & 6.5685 & 5.0842 \\
1D-CNN & 0.3765 & 6.5050 & 4.7157 \\ TransCNN & 0.4036 & 6.3620 & 4.7035 \\ NPTransCNN & 0.4035 & 6.3625 & 4.6679 \\ GPTransCNN & 0.4329 & 6.2037 & 4.4975 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of ablation study. with dual-branch architectures outperform their single-branch counterparts. Among them, GPTransCNN delivers the best performance, further validating that the integration of the GPTransformer's global modeling capability with the 1D-CNN's fine-grained local feature extraction substantially improves the model's ability to capture complex spatial heterogeneity and enhances prediction stability. Through systematic ablation experiments, this study validated the effectiveness of two core architectural designs. First, the introduction of geographic positional encoding not only enhanced the model's spatial awareness and its ability to capture spatial distribution patterns but also improved its generalization performance under complex terrain conditions.