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
Parents
562
Gongbo Chen, Shanshan Li, Luke D. Knibbs et al. · The Science of The Total Environment · 2018 · 588 citations · Full text
Natural Sciences, Earth Sciences, Environmental Sciences +16
1–5 of 562
2.4K
6
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Ulsan National Institute of Science and Technology, South Korea
6 publications · 398 citations
6
h-index
5
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4
h-index
4
h-index
1–5 of 2.4K
1.1K

Chinese Academy of Sciences
Beijing, China
18 h-index
67 publications
7.4K citations
University of Chinese Academy of Sciences
Beijing, China
10 h-index
32 publications
2.8K citations
Tsinghua University
Beijing, China
10 h-index
25 publications
1.5K citations

Peking University
Beijing, China
9 h-index
30 publications
1.5K citations

Centre National de la Recherche Scientifique
Paris, France
8 h-index
24 publications
1.6K citations
University of Malaya
Kuala Lumpur, Malaysia
7 h-index
15 publications
675 citations

Ulsan National Institute of Science and Technology
Ulsan, South Korea
7 h-index
12 publications
607 citations
Roma Tre University
Rome, Italy
7 h-index
10 publications
1.1K citations
1–8 of 1.1K
19 publications · 1.7K citations
17
h-index
18 publications · 1K citations
17
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15 publications · 2K citations
14
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ESPR · 15 publications · 683 citations
14
h-index
12 publications · 1.2K citations
12
h-index
1–5