Open AccessA framework for forecasting the hourly nodal water demand and improving the performance of real-time hydraulic models considering model uncertainty
Author(s) -
Cai Jian,
Jinliang Gao,
Yongpeng Xu,
Liqun Deng
Publication year - 2022
Publication title -
journal of hydroinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 50
eISSN - 1465-1734
pISSN - 1464-7141
DOI - 10.2166/hydro.2022.033
Subject(s) - computer science , kalman filter , data assimilation , node (physics) , process (computing) , data mining , data processing , uncertainty analysis , simulation , engineering , artificial intelligence , physics , structural engineering , meteorology , operating system
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