Leveraging Supervised Learning to Optimize Urban Greening Strategies for Combined Sewer Overflow Pollution Reduction

Authors

G. Yetman (ORCID: https://orcid.org/0000-0002-5270-6975)
Patricia J. Culligan (ORCID: https://orcid.org/0000-0002-3647-0640)
Wade R. McGillis (ORCID: https://orcid.org/0000-0002-7591-7281)
Haokai Zhao (ORCID: https://orcid.org/0000-0002-6868-4379)
Siyan Wang (ORCID: https://orcid.org/0000-0003-2590-5175)

Institutions

University of Notre Dame (US)
Lamont-Doherty Earth Observatory (US)
Northwest University (CN)
Massachusetts Institute of Technology (US)
Columbia University (US)

Publication Details

Journal: Water
Published: 2026-04-22
DOI: https://doi.org/10.3390/w18090994
Primary Topic: Urban Stormwater Management Solutions
Type: article
Field-Weighted Citation Impact: 0.00

Funders

National Science Foundation
University of Notre Dame
Fu Foundation School of Engineering and Applied Science

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article

Leveraging Supervised Learning to Optimize Urban Greening Strategies for Combined Sewer Overflow Pollution Reduction

G. Yetman, Patricia J. Culligan, Wade R. McGillis, Haokai Zhao et al.

Apr 22, 2026

Water

Urban Stormwater Management Solutions

article

Leveraging Supervised Learning to Optimize Urban Greening Strategies for Combined Sewer Overflow Pollution Reduction

G. Yetman, Patricia J. Culligan, Wade R. McGillis, Haokai Zhao, Siyan Wang

April 22, 2026article en

Abstract

No abstract available for this paper.

WaterVol. 18(9)

University of Notre Dame (US), Lamont-Doherty Earth Observatory (US), Northwest University (CN), Massachusetts Institute of Technology (US), Columbia University (US)

National Science Foundation, University of Notre Dame, Fu Foundation School of Engineering and Applied Science

Sustainable cities and communities

Openalex Percentile: Top 10%

Urban Stormwater Management Solutions

DOI Open Access PDF OpenAlex

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