Machine Learning For Environmental Data

Machine Learning for Environmental Data is a computational methodology that applies machine learning techniques to the analysis, modeling, and interpretation of environmental data in order to extract meaningful patterns, make predictions, and support decision-making in environmental science.

562

Publications

30K

Citations

2.4K

Authors

1.1K

Institutions

Publications per year

2017–2026

461

Authors

2.4K

  1. CM
    Cosimo Magazzino

    Roma Tre University, Italy

    6 publications · 888 citations

    6

    h-index

  2. KH
    Kyung Hwa Cho

    Ulsan National Institute of Science and Technology, South Korea

    6 publications · 398 citations

    6

    h-index

  3. MM
    Marco Mele

    Roma Tre University, Italy

    5 publications · 860 citations

    5

    h-index

  4. AJ
    Alex J. Cannon

    Environment and Climate Change Canada, Canada

    4 publications · 246 citations

    4

    h-index

  5. KG
    Karina Gibert

    Universitat Politècnica de Catalunya, Spain

    4 publications · 189 citations

    4

    h-index

1–5 of 2.4K

Venues

  1. The Science of The Total Environment

    19 publications · 1.7K citations

    17

    h-index

  2. Journal of Cleaner Production

    18 publications · 1K citations

    17

    h-index

  3. Environmental Science & Technology

    15 publications · 2K citations

    14

    h-index

  4. Environmental Science and Pollution Research

    ESPR · 15 publications · 683 citations

    14

    h-index

  5. Water Research

    12 publications · 1.2K citations

    12

    h-index

1–5