Machine Learning Inflow Forecasting
Machine Learning Inflow Forecasting is a computational forecasting methodology that employs machine learning algorithms to predict reservoir inflow by learning complex hydrological patterns from historical and real-time data, distinguishing itself from traditional statistical or physically-based models through its data-driven, adaptive, and non-linear modeling approach.
188
Publications
15.5K
Citations
643
Authors
336
Institutions
Publications per year
2017–2026
112
Parents
188
Holger R. Maier, Ashu Jain, Graeme C. Dandy et al. · Environmental Modelling & Software · 2010 · 878 citations
Natural Resources, Hydrology, Water Resources Management +17
Mohammad Valipour, Mohammad Ebrahim Banihabib, Seyyed Mahmood Reza Behbahani · Journal of Hydrology · 2012 · 844 citations
Zaher Mundher Yaseen, Sadeq Oleiwi Sulaiman, Ravinesh C. Deo et al. · Journal of Hydrology · 2018 · 694 citations · Full text
Natural Resources, Hydrology, Water Resources Management +17
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643
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336
University of Malaya
Kuala Lumpur, Malaysia
12 h-index
23 publications
720 citations

National Taiwan University
Taipei, Taiwan
9 h-index
26 publications
1.1K citations
University of Tabriz
Tabriz, Iran
6 h-index
11 publications
853 citations

National University of Malaysia
Bangi, Malaysia
5 h-index
15 publications
1.6K citations
United Arab Emirates University
Al Ain, United Arab Emirates
5 h-index
8 publications
266 citations

Universiti Tenaga Nasional
Kuala Selangor, Malaysia
5 h-index
7 publications
257 citations

University of Regina
Regina, Canada
4 h-index
11 publications
216 citations

Institut National de la Recherche Scientifique
Québec, Canada
4 h-index
9 publications
1.6K citations
1–8 of 336
25 publications · 5.3K citations
23
h-index
21 publications · 1.7K citations
20
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8 publications · 220 citations
8
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10 publications · 536 citations
8
h-index
5 publications · 193 citations
5
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