A directed weighted network approach for hazard chain risk assessment including heavy rainfall induced geological disasters and flooding.

With the increasing frequency of extreme climatic events in cities, the heavy rainfall-geological hazard-flood urban hazard chain has become more prominent, while traditional single-hazard assessments fail to reveal its systemic transmission mechanisms. This study constructs a hazard chain risk assessment framework based on directed weighted networks to support precise urban disaster identification and control. Using Event Tree Analysis (ETA), 23 key hazard nodes were identified to build a causal loop network, and a Bayesian network was developed to quantify node dependencies. The disaster-bearing body was divided into actual and functional subsystems, and its exposure-vulnerability and disaster resistance capacity were evaluated using an improved Analytic Hierarchy Process and the brittle entropy method. A multi-influence matrix integrating node degree, shortest path, and global influence was designed to calculate the risk weights of 38 nodes and establish the directed weighted network. Applied to Mentougou District, Beijing, the comprehensive hazard chain risk value was calculated as 34.23, and key high-risk nodes were identified. The results show that this model surpasses traditional unweighted or single-hazard methods by enabling dynamic and quantitative evaluation of complex urban hazard chains, offering new insights for enhancing urban resilience and disaster prevention.