Disaster Occurrence Detection through GNN Models using Disaster Knowledge Graphs

In the context of the increasing scale and complexity of disasters caused by rapid climate change, a comprehensive understanding of disaster big data is essential for effective detection and response. The disaster knowledge graph proposed in this paper fills this gap by capturing the connections between various disaster-related data sources and their potential for growth across heterogeneous datasets. We generate time-series disaster graphs every minute using SNS data (e.g, Twitter) with a specific focus on disasters. Then, we create disaster knowledge graphs to represent the relationships between various data sources and try to predict their potential developments. For disaster detection, we label and annotate knowledge graphs and then detect sudden changes in time-series disaster knowledge graphs. To that end, we assess the effectiveness of three state-of-the-art GNN models for graph-based event classification using Graph Convolutional Network (GCN), Graph Attention Network (GAT), and SageConv. Our experiments show promising results in detecting disaster events using structural data and connectivity patterns within disaster graphs. As a result, our approach can combine the strength of GNNs with a curated disaster knowledge graph to allow for a thorough analysis of real-time social media data for better disaster management and response strategies.