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

Authors

268

  1. AE
    Ahmed El‐Shafie

    University of Malaya, Malaysia

    10 publications · 576 citations

    10

    h-index

  2. FC
    Fi‐John Chang

    National Taiwan University, Taiwan

    4 publications · 212 citations

    4

    h-index

  3. GL
    Gwo‐Fong Lin

    National Taiwan University, Taiwan

    4 publications · 114 citations

    4

    h-index

  4. AN
    Aboelmagd Noureldin

    Royal Military College of Canada, Canada

    4 publications · 353 citations

    4

    h-index

  5. SG
    Shenglian Guo

    Wuhan University, China

    3 publications · 133 citations

    3

    h-index

1–5 of 268

Venues

  1. Water Resources Management

    12 publications · 799 citations

    12

    h-index

  2. 8

    h-index

  3. Journal of Hydrology

    5 publications · 496 citations

    6

    h-index

  4. Water

    4 publications · 192 citations

    4

    h-index

  5. Journal of Hydro-environment Research

    3 publications · 123 citations

    3

    h-index

1–5