## 4 Results and Discussion
### Prediction of Optimal Planting Zones for Se-Rich Crops
#### 4.2.2 Machine Learning
The PDP trend of Mo appears opposite to its actual effect in predictions, possibly due to its uneven spatial distribution, which may limit the PDP's ability to reflect its true impact. Similarly, the SVR model used the same set of input vectors to predict Se crop content. The hyperparameters of the SVR model were determined by using the grid search approach. A radial basis function (RBF) kernel was selected as the kernel function, and the complete hyperparameters are provided in Table 14. A learning curve method was used to monitor model performance and avoid overfitting. The schematic diagram of the SVR model is shown in Figure 4c. Using the SVR model, Se crop content was predicted, and interpolation was applied to generate a Se crop content distribution map for the study area. The prediction indicated Se content ranged from 0.036 to 0.112 mg/kg. The geometric interval method was used to classify Se content into three levels [13]; the boundary between Se-low crop planting zones and second-class Se-rich crop zones was used as the threshold (0.044 mg/kg) to identify Se-rich crops. First-class Se-rich crop planting zones, second-class Se-rich crop planting zones, and Se-low crop planting zones covered 77.60 km\({}^{2}\), 176.80 km\({}^{2}\),and 121.46 km\({}^{2}\), accounting for 20.65%, 47.04%, and 32.32% of the study area, respectively (Figure 5c). The first-class Se-rich crop planting zones predicted by SVR were generally consistent with those predicted by the BP-NN, with the main difference being the larger area predicted by SVR. SHAP values were also calculated to assess feature importance in the SVR model. The SHAP value bar chart (Figure 5e) showed that root soil Se content had the highest contribution to prediction (SHAP = 0.0086). The PDPs revealed (Figure 7) that Se has a clear positive impact on the predictions, while Mo, P, S, and stratum exhibit overall negative trends. Compared to the BP-NN model, the SVR model has a potentially limited ability to capture complex nonlinear interactions. Figure 6: The PDP presentation of input features the BP-NN model. Figure 7: The PDP presentation of input features of the SVR model. The performance of the two ML models was evaluated using the coefficient of determination (R\({}^{2}\)), mean absolute error (MAE) and root mean square error (RMSE). The BP-NN achieved an R\({}^{2}\) of 0.77, an MAE of 0.0148, and an RMSE of 0.0191, while the SVR model achieved an R\({}^{2}\) of 0.71, an MAE of 0.0157, and an RMSE of 0.0218. Both models demonstrated comparable parameters, low prediction errors, and high fitting accuracy, indicating acceptable reliability of the predictions. Additionally, we compared the performance of our models with those reported in previous studies. For example, [PERSON] et al. (2022) applied both an artificial neural network (ANN) and a multiple linear regression (MLR) model for similar prediction tasks, reporting R\({}^{2}\) values of 0.77 for ANN and 0.67 for MLR. In comparison, our BP-NN and SVR models achieved R\({}^{2}\) values of 0.77 and 0.71, respectively, indicating not only comparable performance with existing ANN models but also notable improvements over traditional regression methods such as MLR [20].