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

Publications

188

1–5 of 188

Authors

643

  1. AE
    Ahmed El‐Shafie

    University of Malaya, Malaysia

    17 publications · 957 citations

    16

    h-index

  2. GL
    Gwo‐Fong Lin

    National Taiwan University, Taiwan

    6 publications · 261 citations

    6

    h-index

  3. PC
    Paulin Coulibaly

    McMaster University, Canada

    4 publications · 926 citations

    4

    h-index

  4. KC
    Kwok‐wing Chau

    Hong Kong Polytechnic University, Hong Kong SAR China

    4 publications · 1.7K citations

    4

    h-index

  5. BB
    Bernard Bobée

    Institut National de la Recherche Scientifique, Canada

    4 publications · 926 citations

    4

    h-index

1–5 of 643

Venues

  1. Journal of Hydrology

    25 publications · 5.3K citations

    23

    h-index

  2. Water Resources Management

    21 publications · 1.7K citations

    20

    h-index

  3. 8

    h-index

  4. Water

    10 publications · 536 citations

    8

    h-index

  5. Neural Computing and Applications

    5 publications · 193 citations

    5

    h-index

1–5