Assessment of Tropical Cyclone Disaster Damage Based on Learnable Inter-City Interaction GNN

Tropical cyclones (TCs) are one of the most frequent disastrous weather events in China, causing widespread damage. Traditional approaches for assessing TC disaster damage treat the TC affected regions as isolated units and ignore inter-regional interactions, resulting in underestimation of complex dynamics in disaster damage assessment. In this paper, we developed an original TC disaster damage dataset, with each sample representing a unique disaster event, incorporating city-specific multi-dimensional features and damage indicators. Then, using provincial administrative divisions in China as examples, we innovatively assigned cities as nodes and constructed inter-city interaction graphs. To align with the physical interactions, a deep learning model named TC-Damage is specifically established. It includes an edge building module and a backbone. The edge building module aims to construct inter-city interaction features from multiple perspectives. The backbone employs a multi-layer Graph Neural Network (GNN) based on Graph Sample and Aggregate (GraphSAGE) and Jumping Knowledge Network (JKNet) to learn comprehensive and hierarchical features of inter-city interactions. A loss function combined with focal loss and node-level loss is proposed to address data imbalance and to enforce representation node distribution. Multiple experiments demonstrate that TC-Damage outperforms other assessment methods and effectively identifies high-contribution factors. Explainability analysis of Super Typhoon Lekima reveals that key edges are adjacent to cities with high disaster factors and social development levels and significantly overlap with edges exhibiting strong inter-city interactions.