Abstract:

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%.

Key words: knowledge graph convolutional network(KGCN), geological earthquake disaster, disaster event, evolutionary result, knowledge graph, graph convolution neural network(GCN)