## 6 Conclusions  
The dynamic scenario modeling of typhoons and their cascading disasters can assist in improving the reliability of emergency decision making. This study proposes a scenario modeling method for typhoon cascading disasters based on online public opinion mining and analysis; this method aims to solve the problems caused by the lack of analyses combining cascading effects and user concern information in previous typhoon scenario modeling. Specifically, we mined online public opinion texts from the Weibo platform regarding Typhoon Bebinca's landfall in Shanghai from 13 to 18 September 2024. Then, we established a binary sentiment analysis model based on Bert-BiLSTM and trained it on the classic Weibo_Senti_100k sentiment dataset. We used the trained model to extract users' sentiments from the online public opinion texts and employed the LDA model and UCI equation to calculate and identify user concern information. Subsequently, we selected node elements (disaster events (\(C\)), external environment (\(E\)), driving factors (\(D\)), emergency activities (\(M\)), emergency targets (\(T\))) for Bayesian networks by analyzing the evolution  
Figure 11: Labeled scenario knowledge element diagram of emergency responses to typhoon cascading disasters. characteristics of typhoons and their cascading disasters based on the stages of online public opinion; following this, we analyzed the scenario evolution process of typhoon cascading disasters and constructed corresponding conceptual evolution models (Figure 8). Finally, DBNs were employed to model and simulate typhoons and their cascading disaster scenarios (Figure 9). The simulation results validated the effectiveness of the cascading disaster scenario model (Figures 10 and 11), proving its reliability in disaster emergency applications. The typhoon cascading disaster scenario model developed in this study focused on analyzing the complex pattern and influencing factors of typhoons and their cascading disasters, and based on the deduction results, we recommend strengthening the management of emergency responses and simultaneously addressing areas prone to different disasters in future emergencies to better cope with complex disaster chains, and also strengthening responses to cascading disasters. This could compensate for the shortcomings of the traditional \"prediction-response\" model and offer more comprehensive and flexible emergency response strategies for typhoons and their cascading disasters. However, this study predominantly focuses on online public opinion, with the calculation of scenario network node probabilities relying on expert experience. Consequently, it bears limitations regarding data diversity and the adaptability of node probability calculation methods. In future research, the authors intend to incorporate multi-modal public opinion data such as images, videos, sensors (IoT), and meteorological reports, even integrating some fusion models (e.g., multi-modal Transformers). The authors also will consider government behavior regulating public opinion to form a closed-loop feedback public opinion environment. Additionally, the authors will explore the application of AI methods in dynamic scenario deduction and work on developing a self-learning, adaptive scenario evolution model. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15137328/s1](https://www.mdpi.com/article/10.3390/app15137328/s1)]([https://www.mdpi.com/article/10.3390/app15137328/s1](https://www.mdpi.com/article/10.3390/app15137328/s1)), Figure S1: Co-occurrence network of positive/negative sentiments; Figure S2: Word cloud of positive/negative sentiments; Figure S3: Sensitivity diagram of 8 main node variables. ; Table S1: Conditional probability table of node variables (partial). Conceptualization, Y.M., S.H. and J.Q. ; methodology, Y.M., S.H. and J.Q. ; validation, Y.M., S.H. ; formal analysis, Y.M., S.H., J.Q. and S.W. ; investigation, Y.M. and S.H. ; resources, Y.M. and S.H. ; data curation, Y.M. and S.H.