Data-driven Inflow Prediction

Data-Driven Inflow Prediction is a predictive modeling approach that belongs to the class of reservoir inflow forecasting methods and is distinguished by its reliance on empirical data and statistical or machine learning techniques to estimate inflow without explicit representation of hydrological processes.

217

Publications

12.4K

Citations

671

Authors

333

Institutions

Publications per year

2017–2026

136

Authors

671

  1. AE
    Ahmed El‐Shafie

    University of Malaya, Malaysia

    10 publications · 576 citations

    10

    h-index

  2. GL
    Gwo‐Fong Lin

    National Taiwan University, Taiwan

    7 publications · 307 citations

    7

    h-index

  3. AN
    Aboelmagd Noureldin

    Royal Military College of Canada, Canada

    4 publications · 353 citations

    4

    h-index

  4. SS
    Soroosh Sorooshian

    University of California, Irvine, United States

    3 publications · 518 citations

    4

    h-index

  5. MT
    Mohammad Taghi Sattari

    University of Tabriz, Iran

    3 publications · 142 citations

    4

    h-index

1–5 of 671

Venues

  1. Journal of Hydrology

    23 publications · 3.3K citations

    20

    h-index

  2. Water Resources Management

    19 publications · 1.3K citations

    19

    h-index

  3. Water

    10 publications · 323 citations

    11

    h-index

  4. 10

    h-index

  5. Hydrological Sciences Journal

    6 publications · 563 citations

    6

    h-index

1–5