## 4 Results
### Bayesian Scenario Network Model for Typhoon Bebinca and Its Cascading Disasters  
In this study, five experts in the field of disaster emergencies were invited to score the above node variables (Table 4) to determine the conditional probability of each node; the partial node variables are shown in Table S1.  
Then, according to the calculated conditional probability, a scenario deduction probability distribution diagram of each node variable was obtained for Typhoon Bebinca and its cascading disaster events using the GeNe software, as shown in Figure 9.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Scenario Stage** & **Scenario Factors** \\ \hline \multirow{6}{*}{Stage1 (\(S_{1}\))} & C: Typhoon Bebinca is generated and may make landfall in coastal areas like Shanghai and Zhejiang. It is considered a strong typhoon \(C_{1}\). \\  & E: Online public opinion platform \(E_{1}\). \\  & D: Public concern about Bebinca being the strongest typhoon since 1949 \(D_{1}\). \\  & M: Government releases warning \(M_{1}\); identification of hidden dangers \(M_{2}\); pre-lowering water level \(M_{3}\). \\  & T: Taking measures to prevent typhoon disasters and reduce losses \(T_{1}\). \\ \hline \multirow{6}{*}{Stage2 (\(S_{2}\))} & C: Typhoon approaches Shanghai and the wind becomes stronger \(C_{2}\). \\  & E: Online public opinion platform \(E_{2}\). \\  & D: Fear of Bebinca being the strongest typhoon since 1949 \(D_{2}\). \\  & M: initiating level I response \(M_{4}\), closing the expressway, suspending the ordering of takeout, and canceling outdoor activities \(M_{5}\). \\  & T: Ensuring personnel safety and taking measures to prevent cascading disasters \(T_{2}\). \\  & C: Urban waterlogging caused by heavy rainfall \(C_{3}\). \\  & E: Online public opinion platform \(E_{3}\). \\  & D: Anxiety about traffic interruption \(D_{3}\). \\  & M: Pre-lowering of water level \(M_{6}\); releasing warning about waterlogging risk \(M_{7}\). \\  & T: Reducing the impact of waterlogging; ensuring the city’s operation \(T_{3}\). \\ \hline \multirow{6}{*}{Stage3 (\(S_{3}\))} & C: Typhoon makes landfall in Pudong \(C_{4}\). \\  & E: Online public opinion platform \(E_{4}\). \\  & D: Panic about personal safety \(D_{4}\). \\  & M: Multi-departmental handling of dangerous situations \(M_{8}\). \\  & T: Restoring social order and reducing threats to life \(T_{4}\). \\  & C: Tidal level rising \(C_{5}\). \\  & E: Online public opinion platform \(E_{5}\). \\  & D: Worry about saltwater intrusion \(D_{5}\). \\  & M: Reinforcing embankment \(M_{9}\); evacuating residents in low-lying coastal areas \(M_{10}\). \\  & T: Preventing saltwater intrusion; protecting important areas \(T_{5}\) \\  & C: Rainstorm \(C_{6}\). \\  & E: Online public opinion platform \(E_{6}\). \\  & D: Anxiety about property losses \(D_{6}\). \\  & M: Draining water \(M_{11}\); opening temporary settlements \(M_{12}\). \\  & T: Restoring drainage function and ensuring the basic needs of residents are met \(T_{6}\). \\ \hline \multirow{6}{*}{Stage4 (\(S_{4}\))} & C: Damage to facilities after typhoon \(C_{7}\). \\  & E: Online public opinion platform \(E_{7}\). \\  & D: Doubts about recovery and reconstruction \(D_{7}\). \\  & M: Repairing power facilities \(M_{13}\); clearing fallen trees \(M_{14}\). \\  & T: Comprehensive restoration of urban infrastructure \(T_{7}\). \\  & C: Waterlogging in waterlogged areas has not subsided \(C_{8}\). \\  & E: Online public opinion platform \(E_{8}\). \\  & D: Concerns about public health \(D_{8}\). \\  & M: Thoroughly draining accumulated water \(M_{15}\); disinfection of affected areas \(M_{16}\). \\  & T: Eliminating hidden health hazards and preventing epidemics \(T_{8}\). \\ \hline \hline \end{tabular}
\end{table}
Table 4: Knowledge element representation of typhoon scenarios and their cascading effects (partial).