HydroTrace用注意力机制提升水文预测精度与可解释性
AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling
- 基于注意力机制构建数据无关的水文模型
- 在青藏高原实现98%纳什效率，泛化能力强
- 支持冰川融雪与季风过程的可视化解释
传统方程驱动的水文模型在青藏高原等复杂区域难以准确预测径流，而现有混合或算法驱动模型则缺乏可解释性。本文提出HydroTrace，一种算法驱动、数据无关的模型，在未见数据上表现优异，达到98%的纳什-萨特克利夫效率。该模型通过先进注意力机制捕捉时空变化与特征影响，可量化并空间解析径流分配，揭示冰川-积雪-径流相互作用及季风动态等水文行为。此外，结合大语言模型的应用使用户能便捷理解与应用其分析结果。HydroTrace显著提升了水文与地球系统建模的预测精度与可解释性。
原文摘要 · Abstract (English)
Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven, data-agnostic model that substantially outperforms these approaches, achieving a Nash-Sutcliffe Efficiency of 98% and demonstrating strong generalization on unseen data. Moreover, HydroTrace leverages advanced attention mechanisms to capture spatial-temporal variations and feature-specific impacts, enabling the quantification and spatial resolution of streamflow partitioning as well as the interpretation of hydrological behaviors such as glacier-snow-streamflow interactions and monsoon dynamics. Additionally, a large language model (LLM)-based application allows users to easily understand and apply HydroTrace's insights for practical purposes. These advancements position HydroTrace as a transformative tool in hydrological and broader Earth system modeling, offering enhanced prediction accuracy and interpretability.
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