Research on Spatiotemporal Knowledge Recommendation for Marine Storm Surge Based on a “Scenario–Response” Framework

Marine storm surge disasters occur frequently with complex and variable scenarios, causing severe casualties and economic losses in coastal areas. However, existing research still has limitations in the integrated analysis of event chain and emergency plan knowledge, the efficiency and accuracy of disaster knowledge extraction, and the intelligence level of knowledge reasoning methods. To address these challenges, this study proposes a “scenario-response” knowledge reasoning method for marine storm surge disasters that integrates event chains and emergency plans. First, disaster event chains and emergency plan processes are structurally modeled to enable unified semantic representation, and a knowledge fusion mechanism is designed to integrate event chains with emergency response procedures. Second, an improved OSS-CasRel knowledge extraction model, enhanced with a domain-specific dictionary, is constructed to extract entities and relations from marine storm surge texts and to build a spatiotemporal knowledge graph. Third, a knowledge reasoning approach based on BERT and downstream text matching models is implemented to generate adaptive and visualized emergency response plans. Experimental results demonstrate that the OSS-CasRel model achieves an accuracy of 80% in entity and relation extraction; in the knowledge graph, the matching overlap rate between the “response” text generated by model reasoning and the original node information exceeds 90%. This study can effectively improve the intelligent emergency response capability for marine storm surge disasters and provide scientific support for emergency decision-making in coastal areas.