## 4 Results and Discussion
### Prediction of Optimal Planting Zones for Se-Rich Crops
#### 4.2.2 Machine Learning  
Similar to the FWofE model, one limitation of ML lies not only in the opacity of its intermediate processes but also in the significant impact of feature selection on prediction accuracy. Before constructing models to predict Se crop content using ML algorithms, preprocessing training data and carefully selecting root soil indicators are essential steps. The crop data were normalized using the max-min normalization method as training data. Figure 5: Graded maps of the prediction results and bar chart of the importance of indicators to the prediction results ((**a**): graded map of the FWofE model prediction results; (**b**): graded map of the BP-NN model prediction results; (**c**): graded map of the SVR model prediction results; (**d**): barchart of the importance of input features to the prediction results in the BP-NN model; (**e**): bar chart of the importance of input features to the prediction results in the SVR model). Root soil indicators used in BP-NN and SVR models were selected based on correlation analysis results and model training performance. From the correlation analysis and geological background, the BP-NN and the SVR model used Mo, Se, P, S, and crop geological strata (the geological strata at the location where the crop samples were collected) as input features. The distributions of the root soil content for Se, Mo, P, and S are shown in Figure 11.  
In the BP-NN modeling, Mo, Se, P, S, and crop geological strata were used as input layer features, with Se crop content as the output variable. The hyperparameters of the BP-NN model were determined through an empirical approach based on the error curves of the training and testing sets, with the aim of avoiding overfitting. The model included two hidden layers, with 12 neurons in the first layer and 6 neurons in the second, and the complete hyperparameters are provided in Table 14. A training-validation error curve method was applied to monitor model performance and avoid overfitting. The schematic diagram of the BP-NN is shown in Figure 4b. The BP-NN model was used to predict Se crop content, and interpolation was applied to generate a distribution map of Se content in the study area. The prediction showed Se crop content ranged from 0.034 to 0.116 mg/kg. The geometric interval method was used to classify Se crop contents into three levels [13]: Se-low crop planting zones, second-class Se-rich crop planting zones, and first-class Se-rich crop planting zones (Figure 5b). Based on the geometric interval classification of the predicted Se content, the boundary between Se-low crop planting zones and second-class Se-rich crop zones was used as the threshold (0.045 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 44.72 km\({}^{2}\), 214.18 km\({}^{2}\), and 116.97 km\({}^{2}\), accounting for 11.90%, 56.98%, and 31.12% of the study area, respectively. First-class Se-rich crop planting zones were concentrated in the north and northeast of the study area, displaying a significant positive spatial correlation with root soil Se and Mo content and a significant negative spatial correlation with P and S content. These results were consistent with the results of Pearson correlation analysis. To enhance the interpretability of the model, SHAP values were used to assess the importance of each input variable in the BP-NN model [53; 54]. In addition, Partial Dependence Plots (PDPs) were introduced to further illustrate the effect of individual variables on the prediction outcomes, thus complementing SHAP by providing insights into the effect direction [55]. The SHAP value bar chart (Figure 5d) revealed that Mo contributed most to the prediction (SHAP = 0.1286), while other features had slightly lower contributions. The PDPs revealed (Figure 6) that Se shows a clear positive effect on the predictions, while Mo, P, S, and stratum exhibit overall negative trends.