文献
J-GLOBAL ID:202002224562077234
整理番号:20A2476371
限られた観測データを持つ大規模流域の日河川流シミュレーションのための物理プロセスと機械学習を組み合わせた水文モデル【JST・京大機械翻訳】
A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data
著者 (6件):
Yang Shuyu
(State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China)
,
Yang Dawen
(State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China)
,
Chen Jinsong
(Earth and Environmental Sciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA)
,
Santisirisomboon Jerasorn
(Division of Energy Engineering, Faculty of Engineering, Ramkhamhaeng University, Bangkok, Thailand)
,
Lu Weiwei
(State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China)
,
Zhao Baoxu
(State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China)
資料名:
Journal of Hydrology
(Journal of Hydrology)
巻:
590
ページ:
Null
発行年:
2020年
JST資料番号:
C0584A
ISSN:
0022-1694
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
オランダ (NLD)
言語:
英語 (EN)