用于分解多尺度系统动力学的分层解耦循环网络：在水文系统中的应用
Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems: An application on Hydrological Systems
AI总结：
提出了一种分层解耦循环网络（FHNN），通过逆模型和正向模型在多时间尺度上分解并捕获系统动力学交互，在水文径流预测任务中优于物理和Transformer基线，且在数据有限时仍能保持准确性。
摘要：
我们提出了一个用于建模多尺度过程的框架，并在水文学的径流预测背景下研究了其性能。具体而言，我们提出了一种新颖的分层循环神经架构，该架构在多个时间尺度上分解系统动力学并捕获它们的相互作用。该框架由一个逆模型和一个正向模型组成。逆模型用于从数据（物理模型模拟、观测数据或两者的历史组合）中经验性地解析系统的时间模式，然后这些状态在正向模型中用于预测径流。在 National Weather Service North Central River Forecast Center 的多个流域上的实验表明，FHNN 优于标准基线，包括基于物理的模型和基于 transformer 的方法。该模型在径流系数较低和气候较冷的流域中表现出特别的效力。我们进一步在 CAMELS（Catchment Attributes and MEteorology for Large-sample Studies，用于大样本研究的流域属性和气象数据）上验证了 FHNN，这是一个广泛使用的大陆尺度水文基准数据集，证实了在各种水文条件下，1-7 天径流预测的性能改进是一致的。此外，我们表明 FHNN 即使在训练数据有限的情况下也能通过有效的预训练策略和训练全局模型来保持准确性。
英文摘要：
We present a framework for modeling multi-scale processes, and study its performance in the context of streamflow forecasting in hydrology. Specifically, we propose a novel hierarchical recurrent neural architecture that factorizes the system dynamics at multiple temporal scales and captures their interactions. This framework consists of an inverse and a forward model. The inverse model is used to empirically resolve the system's temporal modes from data (physical model simulations, observed data, or a combination of them from the past), and these states are then used in the forward model to predict streamflow. Experiments on several catchments from the National Weather Service North Central River Forecast Center show that FHNN outperforms standard baselines, including physics-based models and transformer-based approaches. The model demonstrates particular effectiveness in catchments with low runoff ratios and colder climates. We further validate FHNN on the CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), which is a widely used continental-scale hydrology benchmark dataset, confirming consistent performance improvements for 1-7 day streamflow forecasts across diverse hydrological conditions. Additionally, we show that FHNN can maintain accuracy even with limited training data through effective pre-training strategies and training global models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
