发表机构
University of Virginia; Chandra Robot Autonomy Lab; Link Lab; Multidisciplinary Research Center; Hydroinformatics Research Group（弗吉尼亚大学; 钱德拉机器人自主实验室; 链接实验室; 多学科研究中心; 水文信息学研究组）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对城市洪水预测，开发物理信息训练框架用于CNN-LSTM模型，嵌入三个可微惩罚项。在诺福克洪水数据集评估，物理约束模型重力违规近零、街道通道召回率最高，地形感知惩罚项协调权衡，解决了总体精度与街道层面合理性的矛盾。
AI 中文摘要
用均方误差损失训练的深度学习替代模型能产生统计上准确但物理上无约束的洪水预测，如水可能向上流动、自发出现或在街道层面走廊上平滑分布。我们为CNN-LSTM模型开发了一个物理信息训练框架，用于预测128x128空间网格上15分钟间隔的城市洪水深度。损失中嵌入了三个可微惩罚项：重力损失、连续性损失和地形感知误报惩罚。在弗吉尼亚州诺福克洪水数据集上评估，结果表明物理约束模型重力违规接近零，街道通道召回率最高，地形感知惩罚项能协调权衡，暴露了总体像素级误差与应用特定物理合理性之间的基本矛盾，并表明地形感知损失调制提供了原则性解决方案。
英文摘要
Deep learning surrogate models trained with mean-squared-error loss produce statistically accurate but physically unconstrained flood predictions: water may flow uphill, appear spontaneously, or smooth over street-level corridors. We develop a physics-informed training framework for CNN-LSTM models that predict urban flood depths at 15 min intervals over a 128x128 spatial grid. Three differentiable penalty terms are embedded into the loss: (i) a gravity loss penalizing depth increases against the water-surface-elevation gradient, (ii) a continuity loss enforcing local mass conservation with rainfall-adaptive thresholds, and (iii) a topography-aware false-alarm penalty modulated by the topographic wetness index (TWI). We evaluate on the Norfolk, Virginia flood dataset spanning two storm events (August 2017 and September 2022, 300 samples), with all variants trained on identical splits and robustness assessed over repeated random splits and leave-one-storm-out tests. A road-proximal evaluation restricted to a TWI-derived street mask quantifies street-level skill. The physics-constrained model achieves near-zero gravity violations (order 1e-6) and the highest street-channel recall (0.77 +/- 0.09 vs 0.44 +/- 0.10 for the unconstrained baseline), the capability most relevant to traffic routing, and its advantage more than doubles on a held-out storm; a uniform false-alarm variant attains 16% lower mean absolute error but suppresses street recall to 0.25. The TWI-modulated penalty reconciles this trade-off: it improves on the uniform variant on every metric, recovering 60% higher street recall at the lowest MAE among constrained variants and the best street-level F1. These results expose a fundamental tension between aggregate pixel-level error and application-specific physical plausibility, and show that terrain-aware loss modulation offers a principled resolution.
Comments23 pages, 9 figures
Journal refWater 18(15), 1809 (2026)
