Data-driven Inflow Forecasting
Data-Driven Inflow Forecasting is a forecasting approach that belongs to the class of inflow prediction methods and is distinguished by its reliance on empirical data and statistical or machine learning models, rather than physical hydrological processes, to predict water inflows into reservoirs or watersheds.
87
Publications
4.3K
Citations
268
Authors
139
Institutions
Publications per year
2017–2026
55
Parents
87
1–5 of 87
268
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139

National Taiwan University
Taipei, Taiwan
7 h-index
16 publications
565 citations
University of Malaya
Kuala Lumpur, Malaysia
6 h-index
7 publications
257 citations
Wuhan University
Wuhan, China
4 h-index
11 publications
478 citations

National University of Malaysia
Bangi, Malaysia
4 h-index
7 publications
337 citations

Universiti Tenaga Nasional
Kuala Selangor, Malaysia
3 h-index
3 publications
119 citations

Royal Military College of Canada
Kingston, Canada
3 h-index
3 publications
160 citations

Indian Institute of Technology Roorkee
Roorkee, India
3 h-index
7 publications
1.1K citations

Iran University of Science and Technology
Tehran, Iran
2 h-index
3 publications
107 citations
1–8 of 139
12 publications · 799 citations
12
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8 publications · 327 citations
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5 publications · 496 citations
6
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4 publications · 192 citations
4
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3 publications · 123 citations
3
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1–5