用本地数据微调全球模型，提升洪水预报精度。
Fine Flood Forecasts: Incorporating local data into global models through fine-tuning
- 先全球预训练，再用局部流域数据微调模型。
- 本地数据可显著提升表现，尤其在原表现差的流域。
- 为国家预报员提供自主优化模型的实用路径。
洪水是最常见的自然灾害，精准的洪水预报对早期预警至关重要。以往研究表明，机器学习（ML）模型在训练于大规模、地理多样化的数据集时，能有效提升洪水预测能力。然而，这种全球训练模式导致国家预报机构难以将模型适配至本地，阻碍了其实际部署。同时，基于物理的水文研究指出，本地数据——通常仅本地机构可获取——对提升模型性能具有价值。为此，本文提出一种方法：先在大型全球数据集上预训练模型，再针对单个流域的本地数据进行微调。结果表明，该方法能显著提升模型性能，验证了本地数据中存在额外信息。尤其在全局训练中表现不佳的流域，改进最为明显。本文为希望使用自身数据掌控全球模型的国家预报员提供了实施路线图，旨在降低基于机器学习的水文预报系统实际应用的门槛。
原文摘要 · Abstract (English)
Floods are the most common form of natural disaster and accurate flood forecasting is essential for early warning systems. Previous work has shown that machine learning (ML) models are a promising way to improve flood predictions when trained on large, geographically-diverse datasets. This requirement of global training can result in a loss of ownership for national forecasters who cannot easily adapt the models to improve performance in their region, preventing ML models from being operationally deployed. Furthermore, traditional hydrology research with physics-based models suggests that local data -- which in many cases is only accessible to local agencies -- is valuable for improving model performance. To address these concerns, we demonstrate a methodology of pre-training a model on a large, global dataset and then fine-tuning that model on data from individual basins. This results in performance increases, validating our hypothesis that there is extra information to be captured in local data. In particular, we show that performance increases are most significant in watersheds that underperform during global training. We provide a roadmap for national forecasters who wish to take ownership of global models using their own data, aiming to lower the barrier to operational deployment of ML-based hydrological forecast systems.
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