Data-driven Hydrologic Forecasting

Data-Driven Hydrologic Forecasting is a hydrologic prediction methodology that, in contrast to physics-based approaches, relies primarily on statistical and machine learning techniques to model watershed behavior directly from observational data without explicit representation of underlying physical processes.

179

Publications

11.7K

Citations

627

Authors

310

Institutions

Publications per year

2017–2026

112

Authors

627

  1. CS
    Chaopeng Shen

    Pennsylvania State University, United States

    7 publications · 910 citations

    7

    h-index

  2. DF
    Dapeng Feng

    Pennsylvania State University, United States

    4 publications · 831 citations

    4

    h-index

  3. KL
    Kathryn Lawson

    Pennsylvania State University, United States

    4 publications · 543 citations

    4

    h-index

  4. GP
    Georgia Papacharalampous

    National Technical University of Athens, Greece

    3 publications · 160 citations

    3

    h-index

  5. HT
    Hristos Tyralis

    National Technical University of Athens, Greece

    3 publications · 160 citations

    3

    h-index

1–5 of 627

Venues

  1. Journal of Hydrology

    25 publications · 2.9K citations

    22

    h-index

  2. Water

    9 publications · 268 citations

    9

    h-index

  3. Water Resources Management

    7 publications · 378 citations

    7

    h-index

  4. Environmental Modelling & Software

    6 publications · 330 citations

    6

    h-index

  5. Hydrology and earth system sciences

    5 publications · 916 citations

    5

    h-index

1–5