## 2 Data and Methods
### Methods
#### 2.3.4 Correlation Analysis  
1. XGBoost algorithm  
XGBoost is a Boosting algorithm belonging to an ensemble learning model. With the most efficient performance on supervised learning tasks such as classification, regression, and ranking, the algorithm has become a tool of choice for machine learning, primarily due to its excellent prediction performance, highly optimized multi-core processing and distributed machine implementation, and the ability to handle sparse data. The XGBoost algorithm is a gradient boosting, gradient lifting method that uses an addition model and a forward distribution algorithm to gradually approach the optimal result. Moreover, the XGBoost algorithm simultaneously prevents model overfitting by introducing a regularization term (a measure of tree model complexity) in the objective function [63], which is calculated as follows:  
\[\hat{G}_{j}^{N}=\sum_{k=1}^{N}\phi_{k}(x_{i})=\hat{G}_{j}^{N-1}+\phi_{N}(x_{j}) \tag{6}\]  
where \(\hat{G}_{j}^{N-1}\) is the generated tree, \(\phi_{N}(x_{j})\) is the newly created tree model and \(N\) is the total number of tree models. In this study, the EVs of the study area were used as dependent variables, and various natural and social factors were used as independent variables. All variables were input into the XGBoost model, and the model was cleaned, normalized and trained on the input data to obtain the determined relationship model. 2. SHAP method  
Machine learning has great research significance and wide application value for time series prediction, but its application in real-world tasks is severely limited due to the lack of interpretability. Interpretability is very important in the ecological field, and research on constructing interpretable deep learning models for EVs can provide complete logic for corresponding decision-making. To explain the extent to which different drivers contribute \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Primary Type** & **Secondary Type** & **Abbreviation** & **CL** & **WO** & **GL** & **WA** & **UL** & **WL** \\ \hline \multirow{3}{*}{Provision} & Food production & FP & 1793.88 & 179.39 & 538.16 & 179.39 & 17.94 & 538.16 \\  & Raw material & RM & 179.39 & 4664.08 & 89.69 & 17.94 & 0.00 & 125.57 \\ \hline \multirow{3}{*}{Regulation} & Gas regulation & GR & 896.94 & 6278.58 & 1435.10 & 0.00 & 0.00 & 3228.98 \\  & Climate regulation & CR & 1596.55 & 4843.47 & 1614.49 & 825.18 & 0.00 & 30,675.33 \\  & Water supply & WS & 1076.33 & 5740.41 & 1435.10 & 36,559.25 & 53.82 & 27,805.12 \\  & Waste treatment & WD & 2941.96 & 2349.98 & 2349.98 & 32,612.71 & 17.94 & 32,612.71 \\ \hline \multirow{3}{*}{Support} & Soil formation and & \multirow{3}{*}{SFR} & 2619.06 & 6996.13 & 3498.06 & 17.94 & 35.88 & 3067.53 \\  & Retention & BD & 1273.65 & 5848.04 & 1955.33 & 4466.76 & 609.92 & 4484.70 \\ \hline Culture & Recreation and culture & EC & 17.94 & 2296.16 & 71.76 & 7785.43 & 17.94 & 9956.03 \\ \hline \multicolumn{2}{c}{Total} & \multicolumn{1}{c}{} & 12,395.70 & 39,196.25 & 12,987.68 & 82,464.60 & 753.43 & 112,494.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EVs per unit area by land use type in the Dongting Lake (Unit: \(\text{Yuan}/\text{hm}^{2}\)). to the machine learning prediction results, the SHAP method was applied to the predictions of XGBoost [64]. The SHAP method belongs to the post-event interpretation framework, which estimates the contribution of each driving factor through the Shapley value, which is the core of SHAP. Compared with traditional feature importance methods, SHAP has better consistency and can calculate the impact of each driver on the prediction results, helping researchers interpret machine learning predictions. Therefore, it can be used to explain the contribution of each driver to the prediction, adding transparency and facilitating the analysis of the degree of response of ESVs to different drivers.