用新型状态空间模型提升降雨-径流模拟精度，超越经典LSTM。
A Deep State Space Model for Rainfall-Runoff Simulations
- 采用频率调谐的对角状态空间序列模型S4D-FT进行水文建模。
- 在531个流域上优于LSTM，在多样地理区域表现更稳定。
- 适合需要高精度水文预测的研究者和水资源管理者。
传统水文研究依赖概念或物理基础模型分析降雨-径流过程。近年来，深度学习在该领域迅速发展，但长期占据基准地位的仍是几十年历史的LSTM网络，其性能仍优于如Transformer等新架构。本文提出一种状态空间模型（SSM），具体为频率调谐对角状态空间序列（S4D-FT）模型，用于降雨-径流模拟。该模型在美利坚合众国本土（CONUS）531个流域上与主流LSTM及物理基萨克拉门托土壤水分会计模型（Sacramento Soil Moisture Accounting model）进行了对比。结果表明，S4D-FT在多样化地理区域均优于LSTM。本研究首次将S4D-FT引入水文模拟，挑战了LSTM在该领域的主导地位，拓展了深度学习在水文建模中的工具箱。
原文摘要 · Abstract (English)
The classical way of studying the rainfall-runoff processes in the water cycle relies on conceptual or physically-based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in hydrology community for rainfall-runoff simulations. However, the decades-old Long Short-Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D-FT) model, for rainfall-runoff simulations. The proposed S4D-FT is benchmarked against the established LSTM and a physically-based Sacramento Soil Moisture Accounting model across 531 watersheds in the contiguous United States (CONUS). Results show that S4D-FT is able to outperform the LSTM model across diverse regions. Our pioneering introduction of the S4D-FT for rainfall-runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.
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