## 3 Results
### Heavy Rain Disaster Risk Assessment
#### 3.2.4 Model Evaluation Result  
The confusion matrix is a widely used method in the machine learning and statistics, providing a structured way to meticulously present the simulation results of classification models and accurately measure the differences between simulated and actual values. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hyperparameter** & **Definition** & **SCV-RF** & **MAML-RF** \\ \hline n\_estimators & Number of decision trees & 14 & 12 \\ max\_depth & The maximum depth of decision tree & 8 & 6 \\ min\_samples\_leaf & The minimum number of samples of leaf nodes & 2 & 2 \\ min\_samples\_split & The minimum number of samples required for internal node splitting & 3 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters settings of the SCV-RF model and MAML-RF model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Object** & **Criterion** & **Indicator** & **Abbreviation** & **Data Source** & **Unit** \\ \hline \multirow{4}{*}{Heavy rain disaster risk} & \multirow{2}{*}{Disaster-causing factors} & Heavy rain days & HRD & [[https://m.data.cma.cn/](https://m.data.cma.cn/)]([https://m.data.cma.cn/](https://m.data.cma.cn/)) & d \\  & Rainfall amount & RFA & accessed on 12 December 2023 & mm \\  & Rainfall contribution rate & RC & & \\ \cline{2-5}  & \multirow{2}{*}{Disaster-prone environment} & Slope & Slope & [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) & \\  & & Impervious surface ratio & ISR & Annual China Land Cover Dataset & \(\text{km}/\text{km}^{2}\) \\ \cline{2-5}  & \multirow{2}{*}{Disaster-bearing body} & Population density & POP & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) & \\  & & GDP density & GDP & [[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/)) & \\  & & Land Use/Land cover & LULC & Annual China Land Cover Dataset & \\ \cline{2-5}  & \multirow{2}{*}{Disaster mitigation capacity} & Road network density & RND & _China Urban Construction Statistical Yearbook_ & \(\text{km}/\text{km}^{2}\) \\  & & Drainage pipe density & DPD & _China Urban Construction Statistical Yearbook_ & \(\text{km}/\text{km}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Heavy rain disaster risk assessment system in the Beijing–Tianjin–Hebei region.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indicator** & **I** & **II** & **III** & **IV** \\ \hline HRD & 0–3 & 3–5 & 5–7 & 7–15 [43] \\ RFA & 100–200 & 200–400 & 400–800 & \(>\)800 [11] \\ RC & 0–0.175 & 0.175–0.305 & 0.35–0.525 & \(>\)0.525 [44] \\ Slope & \(<\)6 & 6–15 & 15–25 & \(>\)25 [45] \\ ISR & 0–0.1 & 0–1.02 & 0–2.03 & \(>\)03 [46] \\ POP & 0–300 & 300–500 & 500–700 & \(>\)700 [47] \\ GDP & 0–2000 & 2000–3000 & 3000–4000 & \(>\)4000 [48] \\ LULC & 42 & 3 & 17 & 56 8 9 [49] \\ RND & 0–3 & 3.0–4.5 & 45–6.0 & \(>\)6.0 [43] \\ DPD & 8\(>\) & 8–6 & 6–4 & 4–2 [43] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of heavy rain disaster risk factors.  
specific results of the confusion matrices for the SCV-RF model and the MAML-RF model on the test set are shown in Figure 6. According to the formulas in Section 2.3.5, the accuracy metrics for each model are calculated as shown in Table 5: The MAML-RF model achieves accuracy, precision, and recall of 91.11%, 91.91%, and 91.11%, respectively, on the test set. These figures represent improvements of 4.44%, 3.71%, and 4.44% over the SCV-RF model. The MAML-RF model reduced the time by 2.06 s compared to the SCV-RF model. This indicates that the improved MAML-RF model provides better output results in the assessment of heavy rain disaster risk in the region. It demonstrates high robustness and generalization capability, making it suitable for evaluating heavy rain disaster risks in this area. Next, the SHAP interpretability algorithm is introduced to explain the MAML-RF model and quantify the impact of risk factors on disaster risk.