Machine Learning in Environmental Research: Common Pitfalls and Best Practices-Reference-Cited by-同舟云学术

Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Published:2023-06-29 Issue:46 Volume:57 Page:17671-17689
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Zhu Jun-Jie¹^ORCID,Yang Meiqi¹^ORCID,Ren Zhiyong Jason¹^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering and Andlinger Center for Energy and the Environment, Princeton University, Princeton, New Jersey 08544, United States

Funder

Water Research Foundation

Andlinger Center for Energy and the Environment, Princeton University

U.S. Department of Energy

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.3c00026

Reference123 articles.

1. Machine Learning: New Ideas and Tools in Environmental Science and Engineering

2. Data Analytics for Environmental Science and Engineering Research

3. Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

4. Machine Learning

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