## 1 Introduction  
As a result of global warming, the frequency and intensity of extreme weather events are increasing [1]. In addition to the accelerated urbanization process, human assets and infrastructure are concentrated in coastal cities [2, 3], making them vulnerable to disasters. When densely populated regions are hit by typhoons or other extreme weather events, the resulting disasters and their cascading effects often cause extensive damage, including severe casualties and economic losses [4, 5].  
Typhoons are \"trigger disasters\" that induce secondary and derivative disasters with cascading effects in urban areas. They cause direct impacts from wind, rainfall, and large waves, and cascading disasters like urban waterlogging, mudslides, and storm surges [6].  
Appropriate emergency decision making and response measures can effectively reduce the negative impacts of typhoons and their cascading effects. Therefore, enhancing the targeting and reliability of decisions and measures in complex disaster environments is critical [7]. However, the current theoretical \"prediction-response\" model, which relies on historical data and predefined calculation, cannot represent dynamic and complex typhoon cascading disasters. This has prompted scholars to shift their focus toward the \"scenario-response\" model in decision-making research [8; 9].  
Scenarios refer to collections of anticipated situations that detail the entire process of an event's occurrence, development, and future [10]. Reliable scenario models can display multiple possible event states and adjust response strategies to dynamically address complex relationships. However, the selective concern mechanisms that take place when decision makers process disaster information tend to exacerbate cognitive fragmentation, resulting in an inconsistent understanding of disasters and measures [11]. Therefore, a more reliable scenario model can be established by studying the characterization of typhoons and the evolution process of their cascading disasters, combined with relevant information about users' sentiments.  
To overcome the above limitations, this study develops a dynamic scenario evolution model for typhoons and their cascading disasters based on public opinion. Bayesian networks have been widely applied in the representation of uncertain problems and offer notable advantages, such as clear graphical representations of complex situations, explicit modeling of causal relationships, and the performance of uncertain inference [12]. These characteristics make it possible to accurately characterize the evolution path of typhoon-induced cascading disasters. Therefore, this study employs DBNs to enhance uncertainty representation and inference in a knowledge representation model of typhoon-induced cascading disasters, establishing a dynamic scenario model. The main contributions of this study include the following:  
(1) Extracting and analyzing information on users' sentiments and concerns about typhoons and their cascading disasters.  
(2) Using knowledge elements to analyze the characteristics of cascading disaster evolution and determine the scenario node elements based on the development of public opinion regarding typhoon events.  
(3) Constructing a scenario model of a typhoon and its cascading disasters and using DBNs to calculate the probability distribution of scenario nodes.  
The remainder of this paper is organized as follows: Section 2 reviews existing research on typhoons' cascading effects and typhoon scenario modeling. Section 3 introduces the study area, the experimental data, the pre-processing procedures, and the overall methodology. Section 4 analyzes and discusses the research findings. Section 5 concludes the study and proposes directions for future improvements.