基于KGCN的地质地震灾害事件演化结果预测  

Prediction of geological earthquake disaster event evolution results based on KGCN

作　　者：邵舒羽 张扬 刘艳[1,2] SHAO Shuyu;ZHANG Yang;LIU Yan(School of logistics,Beijing Wuzi University,Beijing 101149,China;Beijing Logistics System and Technology Key Laboratory,Beijing 101149,China)

机构地区：[1]北京物资学院物流学院,北京101149 [2]北京市物流系统与技术重点实验室,北京101149

出　　处：《中国安全科学学报》2025年第2期212-219,共8页China Safety Science Journal

基　　金：国家自然科学基金资助(82102176);北京市社会科学基金资助(21GCL040)。

摘　　要：为提高地质地震灾害事件预测的准确性和可靠性,提出一种结合知识图谱和图卷积神经网络(GCN)的预测模型。首先,构建地质地震灾害事件的知识图谱,将灾害相关的多源信息整合成结构化数据;然后,利用知识图谱卷积网络(KGCN)模型,对知识图谱中的实体和关系进行深度学习,挖掘潜在的关联规则,从而预测灾害的演化结果;最后,通过地质地震灾害事件案例集验证模型的有效性。结果表明:结合知识图谱和GCN的预测模型在地质地震灾害事件演化结果预测方面展现出显著效果,特别是在处理复杂的多源数据时,模型能够高效整合信息,准确挖掘潜在关联,在灾害等级、伤亡等级、承灾体类别等多个方面的预测准确率均表现优异,尤其是在灾害应急响应等级的预测上,准确率达到89.92%。To enhance the accuracy and reliability of geological earthquake disaster events predictions,a predictive model combining knowledge graph with GCN was proposed.Initially,the knowledge graph for geological earthquake disaster events was constructed,and the multi-source disaster-related information was consolidated into structured data.Then,the KGCN model was employed for deep learning of entities and relationships within the knowledge graph,uncovering potential association rules to forecast the evolution of disasters.Finally,the effectiveness of the model was validated through a set of geological earthquake disaster cases.The results show that the predictive model combing knowledge graphs with GCN exhibits excellent effectiveness in forecasting the evolution of geological earthquake disaster events,especially in dealing with complex multi-source data.The information can be efficiently integrated,and potential relationships can be accurately uncovered by the model.Excellent prediction accuracy is achieved in various aspects,including disaster levels,casualty levels,and disaster victim categories.Notably,the accuracy in predicting the disaster emergency response levels reaches 89.92%.

关 键 词：知识图谱卷积网络(KGCN) 地质地震灾害 灾害事件 演化结果 知识图谱 图卷积神经网络(GCN) 

分 类 号：X915.5[环境科学与工程—安全科学]