发表机构
Princeton University; The University of Alabama（普林斯顿大学; 阿拉巴马大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
AutoCF是一个自动化LLM辅助生态系统，集成了数据协调、模型构建、评估和归因，用于复合沿海洪水模拟，在哈维飓风案例中实现了高精度，并揭示了降水主导暴露、沿海强迫主导深水淹没的驱动机制。
AI 中文摘要
复合沿海洪水（CCF）源于沿海、降水和河流过程的相互作用，然而建模工作流往往将模拟、评估和影响分析分离。我们提出了AutoCF，一个自动化生态系统，集成了数据协调、模型构建、观测评估、暴露分析、完整因子驱动归因和跨平台执行。自动化的哈维飓风模拟在八个观测测站上实现了中位均方根误差0.147米和相关系数0.951，并与55个高水位标记的相关系数为0.942。CPU和GPU实现的最大水位场在98.8%的单元格内差异在0.01米以内。归因分析表明，在哈维飓风期间，降水主导了建筑和人口暴露，而沿海强迫对深水和持续淹没变得越来越重要。引入的驱动影响转移指标进一步量化了每个驱动因子相对于其淹没面积贡献，是否对社会后果产生了不成比例的贡献。总体而言，AutoCF提供了一条从CCF模型构建到评估和特定驱动因子影响解释的可复现路径。
英文摘要
Compound coastal flooding (CCF) arises from interacting coastal, precipitation, and river processes, yet modeling workflows often separate simulation, evaluation, and impact analysis. We present AutoCF, an automated ecosystem integrating data harmonization, model construction, observational evaluation, exposure analysis, complete factorial driver attribution, and cross-platform execution. The automated Hurricane Harvey simulation achieves a median root mean square error of 0.147 m and correlation of 0.951 across eight observational gauges, and a correlation of 0.942 with 55 high-water marks. Maximum water-level fields from CPU and GPU implementations agree within 0.01 m for 98.8% of cells. Attribution analysis shows that during Harvey, precipitation dominated building and population exposure, whereas coastal forcing becomes increasingly important for deep and persistent inundation. The introduced Driver Impact Shift metric further quantifies whether each driver contributed disproportionately to societal consequences relative to its flooded-area contribution. Overall, AutoCF provides a reproducible pathway from CCF model construction to evaluation and driver-specific impact interpretation.
Comments35 pages, 10 figures, 15 tables
