Research on a Dynamic Prediction Method for Rainstorm Disaster Chains Based on LLM-Optimized Sliding Window and Dynamic Bayesian Network

Rainstorm-induced disaster chains are characterized by high suddenness, immense destructive power, and complex chain propagation mechanisms. Traditional static assessment methods rely on fixed parameters and struggle to depict the dynamic evolution of such disasters. Existing dynamic models are mostly based on predefined structures and lack the capability to integrate multi-source data and quantify uncertainty, thereby constraining the accurate prediction of rainstorm disaster chains. To address these issues, this study proposes a rainstorm disaster chain prediction model (SW-DBN) that integrates a large language model (LLM)-optimized sliding window mechanism with a dynamic Bayesian network (DBN). The model first performs dynamic segmentation and feature extraction on multi-source time-series data through the sliding window mechanism and constructs an LLM-driven module for semantic understanding of multi-source information and latent parameter mining. By leveraging the LLM’s in-depth analysis of data pattern variations within the window, the model excavates latent parameters, adaptively adjusts the DBN network topology, and feeds back to optimize the window width and sliding step, thereby maintaining adaptive alignment between the sliding window’s feature extraction and the dynamic evolution of the disaster chain. Ultimately, the cascade propagation process of the rainstorm disaster chain is modeled, reasoned, and validated through the DBN, forming an integrated prediction framework of “perception–reasoning, dynamic regulation, and cascade verification.” A case study in the Xi’an area demonstrates that the proposed model can effectively simulate the temporal evolution of rainstorm disaster chains. The average prediction accuracy for four key types of disaster nodes reaches 84.8%, representing an improvement of 7.5 percentage points over the standard DBN model, with clear advantages in early warning timeliness for critical nodes. The proposed model provides technical support for the probabilistic prediction of rainstorm disaster chains and disaster prevention decision-making, featuring both dynamic adaptability and interpretability.