ArticlePloS one2026
Identifying key factors in building fires: A novel approach fusing K-shell entropy gravity.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Building fire key factors are the fundamental control variables that govern both the initiation of fires and dynamics of propagation. The accurate identification of key factors in building fires is crucial for enhancing the effectiveness of fire prevention strategies. To improve the accuracy of key factor identification in building fires, a novel K-shell Entropy Gravity (KEG) algorithm that integrates multiple topological metrics is proposed in this study. First, a complex network is constructed to characterize the relationships among accident factors, where nodes represent influencing factors and edges denote their co-occurrence in fire incidents. Subsequently, considering the positional importance and core connectivity of nodes, the information influence and irreplaceability of nodes, as well as the collaborative coupling and nonlinear characteristic among multiple indicators, a composite attribute integrating K-shell value, information entropy difference, and total shortest path length is developed to quantify node importance, thereby capturing both the local coreness and the global influence of nodes within the network. Then, these metrics are incorporated into an established gravity-based model to comprehensively assess the influential scope of each node, and the results are employed to identify the key factors. Finally, the proposed method is compared with baseline methods based on the Susceptible-Infected-Recovered (SIR) model and network robustness evaluation using the California Building Fire Dataset (2012-2024). In addition, a sensitivity analysis is performed to investigate how the removal of key factors affects accident propagation. To further verify the robustness of this method, fire data from Alaska are applied for comparison, and an ablation experiment is designed. The results indicate that the KEG algorithm achieves superior accuracy in identifying critical factors and offers a reliable analytical tool for developing targeted fire prevention and mitigation strategies.
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