DELUGE：通过基础模型嵌入的可解释条件实现大陆尺度每日暴雨洪水灾害预测

中文摘要 AI 辅助

暴雨洪水占美国国家洪水保险计划（NFIP）索赔的45%，且比河流和沿海洪水更难预测，现有方法存在局限。我们提出DELUGE，这是一个多模态深度学习框架，用于在约1公里分辨率和国家尺度上进行每日暴雨洪水灾害预测。它基于2017 - 2022年经时空校正的NFIP索赔数据训练，围绕灾害风险的危险、暴露和脆弱性组件构建。我们对美国本土100个最高索赔的75公里单元格建模，占暴雨洪水索赔总额的约81%。该框架在水文气象分支中有一对参数模块，通过地形描述符和AlphaEarth基础模型嵌入进行条件设定，具有可解释性。在空间块留出验证下，DELUGE在精确召回曲线下的美元加权面积（PR - AUC）上比调优的随机森林、XGBoost和LightGBM基线高出9%至30%。此外，这种可解释条件设定方案可用于其他地理空间预测任务。

英文摘要

Pluvial (rainfall-driven) flooding accounts for 45% of National Flood Insurance Program (NFIP) claims in the United States and is harder to predict than its riverine and coastal counterparts, with existing approaches limited to coarse resolution, regional domains, or computationally intensive process-based models unsuitable for daily continental-scale use. We present DELUGE, a multimodal deep learning framework for daily pluvial flood damage prediction at ~1 km resolution and national scale, trained on spatially and temporally corrected NFIP claims (2017-2022) and structured around the hazard, exposure, and vulnerability components of disaster risk. Rather than blanket coverage of the Conterminous United States (CONUS), we model the top 100 highest-claim 75 km cells, distributed nationwide and accounting for ~81% of total pluvial flood claims. Our architectural novelty is a pair of parametric modules in the hydrometeorology branch, a Value Modulator and a Temporal Modulator, conditioned on terrain descriptors and AlphaEarth foundation-model embeddings, that expose directly inspectable hydrological response parameters and provide architecture-level interpretability-by-design. Under a spatial block holdout, DELUGE outperforms tuned Random Forest, XGBoost, and LightGBM baselines by 9% to 30% on a dollar-weighted area under the precision-recall curve (PR-AUC), a metric that emphasizes the rare, high-cost claims of greatest operational interest. Beyond DELUGE, we argue this interpretable conditioning scheme is a transferable pattern for integrating foundation-model embeddings into other geospatial prediction tasks.

发表机构

• University of California, Davis（加利福尼亚大学戴维斯分校）

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