城市内涝事理图谱构建方法及应用

为消除城市内涝事件的突发性和空间易变性对城市内涝灾害决策调度的影响，构建城市内涝事理图谱，并在此图谱上提出成因分析应用的框架。利用规则模板库抽取中文城市内涝语料库中的因果事件句，基于投票机制的深度神经网络融合方法抽取因果句中的事件，融合手工规则实现城市内涝事理图谱的构建。采用事理图谱自动生成以内涝点为中心的场景，用生成的场景自动生成并训练离散动态贝叶斯网络，并在该网络上进行内涝点成因分析。结果表明，所构建的城市内涝事理图谱能较好地描述所在城市的内涝演化规律，成因分析结果与真实结果对比表明，此方法能准确找到内涝点处产生内涝的成因，并排除有干扰影响的伪成因。

Construction method and application of event logic graph for urban waterlogging

In order to eliminate the impact of emergency and spatial variability of urban waterlogging events on causal analysis, a framework for constructing the event logic graph and analyzing the causes of waterlogging based on the graph was proposed in this study. The rule template library was used to extract sentences containing causal events in Chinese urban waterlogging corpus, the events in the causal sentences were extracted based on the deep neural network fusion method with voting mechanism, and after that manual rules were combined to construct the event logic graph for urban waterlogging. Then, the event logic graph was used to generate scenes centered on the waterlogging point, which was further exploited to automatically generate and train the discrete dynamic Bayesian network. Finally, this study performed causal analysis based on this network. The result shows that the event logic graph for urban waterlogging well represents the mechanism of urban waterlogging evolution. In addition, the comparison of inferred results and real results shows that this method can accurately find the causes and eliminate the influences of pseudo-positive causes.