Identifying Urban Cascading Disaster Risks From the Past Disaster Processes: A Process Mining Approach

This study aims to propose integrated process mining (IPM) approach, a novel process mining method that integrates knowledge graph, and tests its performance of model generation and utility of the generated model in identifying urban cascading disaster risks. To achieve the goal, we evaluated the effectiveness of the IPM through three sets of experiments. The first experiment tests the effectiveness of the proposed method in generating reliable disaster process models using real data of eleven Chinese cities with largely varying city features. Second, we examine whether the generated model works effectively in identifying cascading disaster risks. The third experiment analyzes performance of the IPM approach under temporal and spatial constraints to understand its applicability scope using data collected from Wuhan, Yantai, and Xiamen city. The results show that (1) the IPM method can generate process models that effectively represent past disaster patterns with even incomplete process trace samples; (2) the generated process models outperform traditional methods in identifying diverse cascading disaster risks and uncovering their complex associations; and (3) performance of the proposed method is strengthened in cities with similar city features and high temporal proximity. Finally, the article provides strategic policy recommendations: (i) Leveraging multi-source media data to decode historical process patterns; (ii) enhancing process mining through domain knowledge transfer to address data constraints; and (iii) implementing spatially and temporally precise risk identification frameworks.