Research on landslide spatial prediction method based on knowledge graph

Landslide spatial prediction is of great significance for disaster prevention and mitigation. Traditional landslide spatial prediction methods rely on the weights of characteristic factors subjectively set by domain experts, and do not consider the correlation between historical landslide cases. This paper combines the knowledge graph link prediction task with the landslide space prediction task, and proposes a landslide space prediction method based on landslide cases knowledge graph and RGCN (Relational Graph Convolutional Network). First, the conceptual modeling of landslide cases is realized by analyzing the domain knowledge of landslides, and the storage of historical landslide cases data is carried out with the graph database as the carrier; secondly, the feature information of the neighborhood nodes and edges of entities in the knowledge graph is aggregated by RGCN, and the graph structure information in the landslide cases knowledge graph is effectively used; finally, the specific cases of loess landslide are selected as the reasoning object to verify the proposed method. The results demonstrate the effectiveness and accuracy of the method in landslide spatial prediction tasks. At the same time, it also provides a new idea for the pre-disaster early warning and hidden danger investigation of landslide disasters.