Towards Generalized Hydrological Forecasting using Transformer Models for 120-Hour Streamflow Prediction
IIHR—Hydroscience and Engineering, University of Iowa（爱荷华大学 IIHR—水科学与工程研究所）；Civil and Environmental Engineering, University of Iowa（爱荷华大学土木与环境工程系）；Electrical and Computer Engineering, University of Iowa（爱荷华大学电气与计算机工程系）
This study explores the efficacy of a Transformer model for 120-hour streamflow prediction across 125 diverse locations in Iowa, US. Utilizing data from the preceding 72 hours, including precipitation, evapotranspiration, and discharge values, we developed a generalized model to predict future streamflow. Our approach contrasts with traditional methods that typically rely on location-specific models. We benchmarked the Transformer model's performance against three deep learning models (LSTM, GRU, and Seq2Seq) and the Persistence approach, employing Nash-Sutcliffe Efficiency (NSE), Kling-Gupta Efficiency (KGE), Pearson's r, and Normalized Root Mean Square Error (NRMSE) as metrics. The study reveals the Transformer model's superior performance, maintaining higher median NSE and KGE scores and exhibiting the lowest NRMSE values. This indicates its capability to accurately simulate and predict streamflow, adapting effectively to varying hydrological conditions and geographical variances. Our findings underscore the Transformer model's potential as an advanced tool in hydrological modeling, offering significant improvements over traditional and contemporary approaches.
Machine Learning for Postprocessing Ensemble Streamflow Forecasts
Earth and Environmental Systems Institute, The Pennsylvania State University（宾夕法尼亚州立大学地球与环境系统研究所）；Environmental Sciences Division, Oak Ridge National Laboratory（橡树岭国家实验室环境科学部）；Energy Systems and Climate Analysis, Electric Power Research Institute (EPRI)（电力研究院（EPRI）能源系统与气候分析部）
Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
AI Research Center（人工智能研究中心）；Advanced College of Engineering and Management（先进工程与管理学院）；Tribhuvan University（特里布万大学）；National College of Engineering（国家工程学院）
AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling
Institute of Tibetan Plateau Research, Chinese Academy of Sciences（中国科学院青藏高原研究所）；China Electric Power Research Institute（中国电力科学研究院）；Department of Earth System Science, Tsinghua University（清华大学地球系统科学系）；University of Gothenburg（哥德堡大学）；National Astronomical Observatories of the Chinese Academy of Sciences（中国科学院国家天文台）；The South China Sea Institute of Oceanology, Chinese Academy of Sciences（中国科学院南海海洋研究所）；North China Electric Power University（华北电力大学）；Global Energy Interconnection Development and Cooperation Organization（全球能源互联网发展合作组织）；Alibaba Cloud（阿里云）
AI Increases Global Access to Reliable Flood Forecasts
Google（谷歌）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）；Institute for Machine Learning, Johannes Kepler University（约翰·开普勒大学机器学习研究所）；RAND Corporation（兰德公司）
Utilizing Earth Foundation Models to Enhance the Simulation Performance of Hydrological Models with AlphaEarth Embeddings
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Infrastructure Engineering, Dalian University of Technology（大连理工大学建设工程学院）；School of Information and Communication Engineering, Dalian University of Technology（大连理工大学信息与通信工程学院）；School of Software, Dalian University of Technology（大连理工大学软件学院）；School of Artificial Intelligence, Dalian University of Technology（大连理工大学人工智能学院）
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）
