用图神经网络建模河流连通性，提升喜马偕尔邦山洪易发区预测精度。
Flood Risk Follows Valleys, Not Grids: Graph Neural Networks for Flash Flood Susceptibility Mapping in Himachal Pradesh with Conformal Uncertainty Quantification
- 构建流域连通图，用图神经网络捕捉上游洪水对下游的影响。
- 模型AUC达0.978，比基线高0.097，证明连通性信息关键。
- 首次生成具有统计保证的90%置信区间地图，适合应急规划者使用。
喜马偕尔邦（HP）是印度最易受山洪灾害影响地区，仅2023年季风季就造成400多人死亡和12亿美元损失。现有风险图将每个像素独立处理，忽略了上游洪水会抬升下游风险的基本规律。本文提出基于流域连通图（460个子流域，1700条有向边）的图神经网络（GraphSAGE），融合六年哨兵-1 SAR洪水数据（2018–2023，共3000次事件）与12个环境变量（30米分辨率）。以四种像素级机器学习模型（随机森林、XGBoost、LightGBM、堆叠集成）为基线，采用留一盆地交叉验证评估，避免随机划分带来的5–15% AUC虚高。通过保形预测，首次生成具有统计保证的90%覆盖区间风险图。图神经网络获得AUC = 0.978 ± 0.017，优于最优基线（AUC = 0.881）和已有研究基准（AUC = 0.88）。+0.097的提升证实河流连通性蕴含重要预测信号。高风险区覆盖1,457公里公路（含217公里曼利-列城走廊）、2,759座桥梁及4处大型水电站。保形区间在2023年测试集上实现82.9%经验覆盖率；高风险区覆盖率偏低（45–59%），提示合成孔径雷达标注噪声是未来改进重点。
原文摘要 · Abstract (English)
Flash floods are the most destructive natural hazard in Himachal Pradesh (HP), India, causing over 400 fatalities and $1.2 billion in losses in the 2023 monsoon season alone. Existing risk maps treat every pixel independently, ignoring the basic fact that flooding upstream raises risk downstream. We address this with a Graph Neural Network (GraphSAGE) trained on a watershed connectivity graph (460 sub-watersheds, 1,700 directed edges), built from a six-year Sentinel-1 SAR flood inventory (2018-2023, 3,000 events) and 12 environmental variables at 30 m resolution. Four pixel-based ML models (RF, XGBoost, LightGBM, stacking ensemble) serve as baselines. All models are evaluated with leave-one-basin-out spatial cross-validation to avoid the 5-15% AUC inflation of random splits. Conformal prediction produces the first HP susceptibility maps with statistically guaranteed 90% coverage intervals. The GNN achieved AUC = 0.978 +/- 0.017, outperforming the best baseline (AUC = 0.881) and the published HP benchmark (AUC = 0.88). The +0.097 gain confirms that river connectivity carries predictive signal that pixel-based models miss. High-susceptibility zones overlap 1,457 km of highways (including 217 km of the Manali-Leh corridor), 2,759 bridges, and 4 major hydroelectric installations. Conformal intervals achieved 82.9% empirical coverage on the held-out 2023 test set; lower coverage in high-risk zones (45-59%) points to SAR label noise as a target for future work.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。