研究水循环中降雨-径流过程的传统方法依赖于概念性或基于物理的水文模型。深度学习（DL）近期作为一种替代方案出现，并在水文界的降雨-径流模拟中蓬勃发展。然而，数十年历史的Long Short-Term Memory（LSTM）网络仍然是该任务的基准，表现优于Transformers等较新架构。在本研究中，我们提出了一种State Space Model（SSM），具体为Frequency Tuned Diagonal State Space Sequence（S4D-FT）模型，用于降雨-径流模拟。所提出的S4D-FT在美国本土（CONUS）的531个流域中，与成熟的LSTM以及基于物理的Sacramento Soil Moisture Accounting模型进行了基准对比。结果表明，S4D-FT能够在不同地区超越LSTM模型。我们开创性地将S4D-FT引入降雨-径流模拟，挑战了LSTM在水文界的主导地位，并扩展了可用于水文建模的DL工具库。
英文摘要：
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.
QDeepGR4J: Quantile-based ensemble of deep learning and GR4J hybrid rainfall-runoff models for extreme flow prediction with uncertainty quantification
School of Mathematics and Statistics, University of New South Wales, Sydney, NSW 2052, Australia（数学与统计学学院，新南威尔士大学，悉尼，新南威尔士州，2052，澳大利亚）；Data Analytics for Resources and Environments, Australian Research Council—Industrial Transformation Training Centre, Sydney, NSW, Australia（资源与环境数据分析，澳大利亚研究理事会—工业转型培训中心，悉尼，新南威尔士州，澳大利亚）
Use of 1D-CNN for input data size reduction of LSTM in Hourly Rainfall-Runoff modeling
International Research Organization for Advanced Science and Technology, Kumamoto University（熊本大学先进科学技术国际研究机构）；Center for Water Cycle, Marine Environment, and Disaster Management, Kumamoto University（熊本大学水循环、海洋环境与灾害管理研究中心）；Department of Civil and Environmental Engineering, University of California, Davis（加州大学戴维斯分校土木与环境工程系）；Graduated School of Science and Technology, Kumamoto University（熊本大学科学与技术研究生院）；Faculty of Advanced Science and Technology, Kumamoto University（熊本大学先进科学技术学部）
A Gated Catchment Hydrology Model (GCHM) for Physically-Interpretable Rainfall-Evapotranspiration-Runoff Simulation Across Diverse Hydroclimatic Regimes
TRU-NET: A Deep Learning Approach to High Resolution Prediction of Rainfall
University of Warwick（华威大学）；Southern University of Science and Technology（南方科技大学）；The European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；University of Bristol（布里斯托大学）
Leveraging Exogenous Signals for Hydrology Time Series Forecasting
College of Computing（计算学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Virginia（弗吉尼亚大学）；School of Data Science（数据科学学院）；Computer Science（计算机科学）；Biocomplexity Institute（生物复杂性研究所）
HydroNets: Leveraging River Structure for Hydrologic Modeling
Google Research（谷歌研究院）；The Hebrew University of Jerusalem（耶路撒冷希伯来大学）；Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）；Technion - Israel Institute of Technology（以色列理工学院）
Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting
CNRS（法国国家科学研究中心）；Nansen Environmental and Remote Sensing Center（南森环境与遥感中心）；Laboratoire d’océanographie et du climat（海洋学与气候实验室）；École nationale supérieure d'informatique pour l'industrie et l'entreprise（国立工业与企业计算机科学高等学院）
Deep learning for improved global precipitation in numerical weather prediction systems
Indian Institute of Tropical Meteorology（印度热带气象研究所）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Technology Bombay（孟买印度理工学院）；Queen Mary University of London（伦敦玛丽女王大学）
Opportunistic Emulation of Computationally Expensive Simulations via Deep Learning
Data61 / CSIRO（联邦科学与工业研究组织 Data61）；Griffith University（格里菲斯大学）；Department of Resources, Queensland Government（昆士兰州政府资源部）；Department of Environment and Science, Queensland Government（昆士兰州政府环境与科学部）
Agricultural Recommendation System based on Deep Learning: A Multivariate Weather Forecasting Approach
Chittagong University of Engineering & Technology（吉大港工程技术大学）；Khulna University of Engineering & Technology（库尔纳工程技术大学）；University of Illinois at Chicago（芝加哥大学伊利诺伊分校）；Khulna Agricultural University（库尔纳农业大学）；Edith Cowan University（伊迪丝·考恩大学）
Do Echo Top Heights Improve Deep Learning Nowcasts?
Kempelen Institute of Intelligent Technologies（智能技术研究所）；Faculty of information Technology（信息技术学院）；Brno University of Technology（布拉格技术大学）；Department of Geoscience and Remote Sensing（地质与遥感系）；Delft University of Technology（代尔夫特技术大学）
Deep Learning Foundation and Pattern Models: Challenges in Hydrological Time Series
Computer Science（计算机科学）；University of Virginia（弗吉尼亚大学）；College of Computing（计算学院）；Georgia Institute of Technology（佐治亚理工学院）；Biocomplexity Institute（生物复杂性研究所）
Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
Swiss Federal Institute of Technology Zurich (ETHZ)（苏黎世联邦理工学院）；Swiss Federal Institute of Aquatic Science and Technology (Eawag)（瑞士联邦水生科学技术研究所）；University of Coimbra（科英布拉大学）
