Fusing Earth observation embeddings and spaceborne terrain with meteorological forcing for flood peak depth prediction from high-water marks
作者: Wen Zhou, Shaowen Wang, Upmanu Lall, Venkatesh Merwade, Dingqi Ye
分类: cs.LG
发布日期: 2026-11-03
💡 一句话要点
提出基于多模态融合的洪水峰值深度预测方法
🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)
关键词: 洪水预测 深度学习 多模态融合 气象强迫 高水位标记 数字高程模型 空间地形 风险管理
📋 核心要点
- 现有洪水深度预测方法主要依赖于后期高水位标记调查，导致数据稀疏且受限于数字高程模型的误差。
- 本文提出了一种基于多模态数据融合的深度学习模型，利用现场观察、空间地形和气象强迫进行洪水峰值深度预测。
- 实验结果表明，该模型在5689个调查标记上实现了平均绝对误差为0.39米，显著优于传统方法。
📝 摘要（中文）
洪水峰值深度在通常干燥的土地上决定了损害和生命风险，但仅通过事件后高水位标记（HWM）调查稀疏观察，且从卫星洪水范围推导的深度在事件后产生，并继承了数字高程模型（DEM）的垂直误差。本文探讨了能否通过现场观察学习洪水峰值深度，并在峰值前从空间地形、地理空间基础模型嵌入和前期气象强迫中进行预测。研究组装了5689个来自39个事件的USGS HWM调查数据，构建了一个跨模态融合网络，预测调查标记处的深度，平均绝对误差为0.39米，提前6小时预测峰值。
🔬 方法详解
问题定义：本文旨在解决洪水峰值深度预测的准确性问题，现有方法依赖于后期高水位标记调查，导致数据稀疏且受限于数字高程模型的误差。
核心思路：通过融合现场观察数据、空间地形信息和气象强迫，构建深度学习模型，提前预测洪水峰值深度，从而克服传统方法的局限。
技术框架：整体架构包括数据收集、特征提取、跨模态融合网络训练和深度预测四个主要模块。数据收集阶段整合了5689个HWM调查数据，特征提取阶段利用Copernicus GLO-30地形和AlphaEarth嵌入。
关键创新：最重要的技术创新在于通过现场调查深度对数字高程模型误差进行隐式校正，显著提高了预测精度。与传统方法相比，该模型能够在没有直接测量的情况下进行有效预测。
关键设计：模型采用了交叉模态融合网络，损失函数设计为平均绝对误差，网络结构包括多个卷积层和全连接层，以提取多层次特征。
🖼️ 关键图片
📊 实验亮点
实验结果显示，模型在5689个调查标记上的平均绝对误差为0.39米，提前6小时进行预测，相较于传统方法显著降低了深度预测误差。模型的有效性在11个未见事件中得到了验证，尽管在深度分辨率上有所下降，但仍能有效区分洪水和无雨条件。
🎯 应用场景
该研究可广泛应用于洪水风险管理、城市规划和应急响应等领域。通过提前预测洪水峰值深度，相关部门可以更有效地制定防洪措施，减少潜在损失。此外，该方法也为未来的气象灾害预测提供了新的思路和工具。
📄 摘要（原文）
Flood peak depth on normally dry land governs damage and risk to life, but is observed only sparsely, by post-event high-water mark (HWM) surveys, and depth derived from satellite flood extent is produced after the event and inherits the vertical error of the digital elevation model (DEM) it is computed from. Deep-learning flood models have in turn been trained on hydrodynamic simulations rather than on observed depths. We ask whether flood peak depth can be learned from field observations and predicted before the peak from spaceborne terrain, geospatial foundation-model embeddings and antecedent meteorological forcing. We assembled 5689 USGS HWM surveys from 39 events (2011-2025) in 24 U.S. states and Puerto Rico, assigned each a peak time from nearby sensors, paired each with a rain-free window at the same site, and linked them to Copernicus GLO-30 terrain, AlphaEarth embeddings and 144 h of hourly forcing masked to the lead time. A cross-modal fusion network trained on these observations predicts depth at the surveyed marks with a mean absolute error of 0.39 m 6 h ahead of the peak. Supervision by surveyed depth corrects the DEM error implicitly: differencing the measured peak water level with the same DEM gives 0.93 m at the same marks, 2.4 times larger, although it uses the measured water level and the model does not. Forcing accounts for 77% of the attributed reduction in depth error; terrain and the embeddings are statistically equivalent to each other and contribute the remaining 23%. On eleven held-out events the model still separates flooded from rain-free conditions but no longer resolves depth at the marks (R2 = 0.03), so the skill reported here is confined to the events and regions sampled. Field-surveyed depths can thus supervise flood depth prediction and correct terrain error that extent-based estimation cannot, while transfer to unseen storms remains the open problem.