# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
## 4 Results
### Model Training and Verification  
A total of 20 rural housing typhoon damage samples from 2014-2018 were selected to train the RBF and BP neural network models, and a total of five typhoon damage samples from 2019 were used for model verification. The predicted values of the BP model were used for comparison. During the training process of the RBF neural network, the number of nodes in the hidden layer can be automatically increased until it reaches the maximum  
Figure 10: Total insured loss rate of rural housing due to typhoons by townships in Ningbo (2014–2019). Figure 9: Zoning map of Ningbo districts and counties. number of nodes in the hidden layer or the accuracy requirement. In the BP neural network training process, in order to make the output results closer to the training target, it is necessary to debug the number of nodes in the hidden layer within the model, the training function, and other related parameters for a long time. When the neural network model is trained, the corresponding training curve can be obtained, as shown in Figure 11. As can be seen from the figure, when the training accuracy was set to 0.001, both models converged under the given conditions, and the BP neural network model needed 110 steps to reach the accuracy requirement. The RBF neural network model required only 12 steps to achieve the same condition, and its training speed was significantly better than that of the BP neural network. The above analysis shows that the RBF neural network has more advantages over the BP neural network in the training speed. To calibrate the models, the predicted values of the BP and RBF neural network models were compared with the actual values (i.e., the average insured loss rate of rural housing under the impact of five typhoons in 2019) and the mean square error was used as the evaluation indicator. The results are shown in Figures 12 and 13. A total of 139 townships in Ningbo were numbered sequentially. It can be seen that both two models could accurately predict the insured loss rate for most townships. However, due to the single input typhoon causal factor (i.e., simulated typhoon wind speed) and the small training sample size (i.e., 20 typhoon damage samples), the two models were less effective in predicting some townships with sudden changes in insured loss rates. In addition, both the BP model and the RBF model had a limited prediction effect for the townships with 0 insured loss rate. Therefore, the predicted values of insured loss rate for some townships were overestimated. In Figure 13, it is noteworthy that the maximum mean square error of the BP model was \(18\times 10^{-6}\), which was less accurate and had a larger fluctuation in the error range than the RBF model with a maximum mean square error of \(8\times 10^{-6}\). Overall, the mean square error of the predicted values of the RBF neural network was smaller than that of the BP neural network. Figure 11: Model training curve. (**a**) BP neural network. (**b**) RBF neural network. Figure 12: Comparison of average insured loss rate by township due to five typhoons in 2019. In order to compare the prediction accuracy of the RBF and BP neural network models more intuitively, the data in Figure 12 were visualized and the zoning maps are shown in Figure 14. In the figure, the RBF prediction results were basically consistent with the actual situation, while in contrast, the BP prediction results had obvious errors with the actual situation; in particular, some townships in the highlighted circles in Figure 14 failed to achieve the loss prediction effect. The most severely affected townships obtained from Table 2 were selected for comparative analysis (see Table 3). According to the comparison, although problems such as small sample size led to some deviations between the predicted values and the actual values in each township, the errors in the predicted values of the RBF model were smaller than those of the BP model in most of the severely affected townships.