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Deep learning for cross-region streamflow and flood forecasting at a global scale.

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第一作者： Binlan,Zhang

第一单位： State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China.;State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China.

作者： Binlan,Zhang ^[1] ; Chaojun,Ouyang ^[2] ; Peng,Cui ^[2] ; Qingsong,Xu ^[3] ; Dongpo,Wang ^[4] ; Fei,Zhang ^[5] ; Zhong,Li ^[6] ; Linfeng,Fan ^[2] ; Marco,Lovati ^[4] ; Yanling,Liu ^[2] ; Qianqian,Zhang ^[7]

作者单位： State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China.;State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China. ^[1] State Key Laboratory of Mountain Hazards and Engineering Resilience, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610299, China. ^[2] Data Science in Earth Observation, Technical University of Munich, 80333 Munich, Germany. ^[3] State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, China. ^[4] State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China. ^[5] Department of Civil Engineering, McMaster University, Hamilton, ON L8S 4L8, Canada. ^[6] Chengdu University of Information Technology, Chengdu 610225, China. ^[7]

关键词 ED-DLSTM cross-region deep learning encoder-decoder double-layer LSTM hydrological regionalization hydrology streamflow forecasting

DOI 10.1016/j.xinn.2024.100617

PMID 39677966

发布时间 2025-01-04

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