A novel comprehensive system for analyzing and evaluating storm surge disaster chains based on complex networks

Against the backdrop of global warming and rising sea levels, storm surge disasters occur frequently, often forming complex chains of events that lead to severe crises. However, systematic research on storm surge disaster chains is scarce. To characterize these chains, this research proposes a storm surge disaster chain analysis system based on complex networks and Bayesian networks. The system consists of three modules: evaluation, prediction, and measurement. The evaluation module uses a complex network model to quantitatively analyze the vulnerability, key nodes, and critical transmission paths of the disaster chain complex network. The prediction module establishes a Bayesian network-based model to forecast the complex network evolution process, forecasting the occurrence probability and loss scenarios of the disaster events. The measurement module measures and calculates the chain effect based on the dependence relationship and loss degree of the disaster event loss scenario. The results elucidate that most key nodes are primary and secondary disasters such as seawater flooding, flooding, dam damage, rainstorm, and house damage. Meanwhile, edges such as the sea wave–seawater flooding and house damage–human casualties have a critical impact on the storm surge disaster chain complex network. Key evolutionary paths such as strong winds–human casualties and over-warning tide level–social influence need to be focused on. Disaster reduction strategies such as maintaining dams, reinforcing houses, and removing disaster-bearing body can effectively break the chain and mitigate disasters. This research has a reference value for the scientific understanding of storm surge disaster chains and can serve as a scientific basis for comprehensive disaster reduction, disaster preparedness, and disaster relief.