AQUAH用自然语言自动完成水文模拟全流程。
AQUAH: Automatic Quantification and Unified Agent in Hydrology
- 基于视觉大模型理解地图栅格，自主完成数据获取与建模
- 从提示到报告全程无需人工干预，结果被专家评为合理透明
- 适合希望快速开展水文模拟的研究者和决策者
我们提出AQUAH，首个面向水文建模的端到端语言驱动智能体。用户仅需输入自然语言指令（如‘模拟2020至2022年小大角河流域洪水’），AQUAH即可自主获取地形、强迫数据与水文站数据，配置水文模型，运行模拟，并生成自包含的PDF报告。该流程由具备视觉理解能力的大语言模型驱动，可实时解析地图与栅格图像，自主决策出流口选择、参数初始化及不确定性分析。在多个美国流域的初步实验表明，AQUAH可实现冷启动模拟并生成可供分析师使用的完整文档，无需人工干预。专家评审认为其输出清晰、透明且物理上合理。尽管仍需进一步校准与验证以用于实际部署，但这些早期成果展示了以大语言模型为核心、具备视觉感知能力的智能体在简化复杂环境建模方面的巨大潜力，有助于降低地球观测数据、物理模型与决策者之间的门槛。
原文摘要 · Abstract (English)
We introduce AQUAH, the first end-to-end language-based agent designed specifically for hydrologic modeling. Starting from a simple natural-language prompt (e.g., 'simulate floods for the Little Bighorn basin from 2020 to 2022'), AQUAH autonomously retrieves the required terrain, forcing, and gauge data; configures a hydrologic model; runs the simulation; and generates a self-contained PDF report. The workflow is driven by vision-enabled large language models, which interpret maps and rasters on the fly and steer key decisions such as outlet selection, parameter initialization, and uncertainty commentary. Initial experiments across a range of U.S. basins show that AQUAH can complete cold-start simulations and produce analyst-ready documentation without manual intervention. The results are judged by hydrologists as clear, transparent, and physically plausible. While further calibration and validation are still needed for operational deployment, these early outcomes highlight the promise of LLM-centered, vision-grounded agents to streamline complex environmental modeling and lower the barrier between Earth observation data, physics-based tools, and decision makers.
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。