Prediction Uncertainty Quantification

Prediction Uncertainty Quantification is a methodological framework that characterizes the degree of confidence in forecasted outcomes by systematically assessing sources of variability and error in predictive models within Early Warning Systems.

928

Publications

52.9K

Citations

2.8K

Authors

1.1K

Institutions

Publications per year

2017–2026

461

Authors

2.8K

  1. SM
    Sankaran Mahadevan

    Vanderbilt University, United States

    8 publications · 255 citations

    8

    h-index

  2. MV
    Mario V. Wüthrich

    ETH Zurich, Switzerland

    6 publications · 205 citations

    6

    h-index

  3. MR
    Mehrdad Raisee

    University of Tehran, Iran

    5 publications · 131 citations

    5

    h-index

  4. NM
    Nicolaas M. Faber

    Radboud University Nijmegen, Netherlands

    5 publications · 308 citations

    5

    h-index

  5. PP
    Pascal Pernot

    Centre National de la Recherche Scientifique, France

    5 publications · 277 citations

    5

    h-index

1–5 of 2.8K

Venues

  1. Water Resources Research

    37 publications · 3.1K citations

    30

    h-index

  2. arXiv (Cornell University)

    Repository · 29 publications · 2.5K citations

    18

    h-index

  3. SSRN Electronic Journal

    Repository · 17 publications · 664 citations

    14

    h-index

  4. Hydrology and earth system sciences

    12 publications · 771 citations

    12

    h-index

  5. Journal of Hydrology

    13 publications · 589 citations

    12

    h-index

1–5