用图变压器提升洪水易发性制图精度，适用于气候变化下的铁路风险评估。
Graph Transformer-Based Flood Susceptibility Mapping: Application to the French Riviera and Railway Infrastructure Under Climate Change
- 基于图注意力机制融合流域拓扑结构，捕捉空间依赖关系。
- 2050年RCP8.5情景下，17.46%区域处于极高易发区，铁路54%线路面临高风险。
- 模型在空间聚类上优于传统方法，适合气候变迁下的基础设施规划。
气候变化导致洪水频发与加剧，威胁基础设施安全，亟需更精准的易发性制图技术。传统机器学习难以刻画空间依赖性且类别边界模糊。本研究首次将图变压器（GT）应用于法国里维埃拉洪水易发区（如2020年风暴亚历克斯影响区），结合地形、水文、地理与环境数据。GT通过拉普拉斯位置编码（PEs）和注意力机制建模流域拓扑结构。模型AUC-ROC达0.9739，略低于XGBoost（0.9853），但空间聚类效果更优，莫兰指数达0.6119（显著优于随机森林0.5775与XGBoost 0.5311，p<0.0001）。特征重要性显示高程、坡度、距河道距离与汇流指数为关键因子。拉普拉斯PEs部分揭示空间簇，但重要性低于洪水相关因子。考虑气候与土地利用变化，预测2050年不同代表浓度路径（RCP）下的易发性地图，并评估铁路线路脆弱性。所有RCP情景均显示各等级易发区面积上升，仅极低区例外。RCP8.5下，17.46%流域面积与54%铁路长度位于极高易发区，较当前条件（6.19%与35.61%）显著增加。生成的地图可融入多灾害评估框架。
原文摘要 · Abstract (English)
Increasing flood frequency and severity due to climate change threatens infrastructure and demands improved susceptibility mapping techniques. While traditional machine learning (ML) approaches are widely used, they struggle to capture spatial dependencies and poor boundary delineation between susceptibility classes. This study introduces the first application of a graph transformer (GT) architecture for flood susceptibility mapping to the flood-prone French Riviera (e.g., 2020 Storm Alex) using topography, hydrology, geography, and environmental data. GT incorporates watershed topology using Laplacian positional encoders (PEs) and attention mechanisms. The developed GT model has an AUC-ROC (0.9739), slightly lower than XGBoost (0.9853). However, the GT model demonstrated better clustering and delineation with a higher Moran's I value (0.6119) compared to the random forest (0.5775) and XGBoost (0.5311) with p-value lower than 0.0001. Feature importance revealed a striking consistency across models, with elevation, slope, distance to channel, and convergence index being the critical factors. Dimensionality reduction on Laplacian PEs revealed partial clusters, indicating they could capture spatial information; however, their importance was lower than flood factors. Since climate and land use changes aggravate flood risk, susceptibility maps are developed for the 2050 year under different Representative Concentration Pathways (RCPs) and railway track vulnerability is assessed. All RCP scenarios revealed increased area across susceptibility classes, except for the very low category. RCP 8.5 projections indicate that 17.46% of the watershed area and 54% of railway length fall within very-high susceptible zones, compared to 6.19% and 35.61%, respectively, under current conditions. The developed maps can be integrated into a multi-hazard framework.
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