Leveraging Supervised Learning to Optimize Urban Greening Strategies for Combined Sewer Overflow Pollution Reduction
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water-18-00994.pdf
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Author(s)
Wang, Siyan
Zhao, Haokai
Yetman, Gregory
McGillis, Wade R.
Culligan, Patricia J.
Date Issued
April 22, 2026
Journal
Water
Publisher
MDPI
Citation
Wang, S.; Zhao, H.; Yetman, G.; McGillis, W.R.; Culligan, P.J. Leveraging Supervised Learning to Optimize Urban Greening Strategies for Combined Sewer Overflow Pollution Reduction. Water 2026, 18, 994.
Version
Final published version
Abstract
Many cities adopt greening strategies to reduce contamination from combined sewer overflows (CSOs). Nonetheless, quantifying the impact of urban greening on CSO-affected water quality at the city scale remains challenging. To address this challenge, this work leveraged supervised learning to link water swimmability with the greening of a CSO shed (the drainage area of a CSO outfall), using New York City (NYC) as a case study. Random forest classification models were built to predict water swimmability after rainfall at 46 sites in NYC water bodies impacted by CSOs. A 14-feature model (AUROC =0.81, accuracy = 0.78) revealed that greening improved local water quality. However, water flow speed, antecedent rain depth, and CSO shed area were also influential. A simplified four-feature model (AUROC = 0.8, accuracy = 0.75) explored links between levels of greening and the probability of non-swimmable waters (𝑃𝑛𝑠) following different 18 h rainfall depths. Increased greening was found to be most impactful in reducing 𝑃𝑛𝑠 for CSO sheds discharging to water bodies with flow speeds < 6 cm/s. For CSO sheds discharging to water bodies with flow speeds ≥ 14.7 cm/s, urban greening had no impact on 𝑃𝑛𝑠. The work illustrates the utility of supervised learning in supporting citywide decisions regarding urban greening investments.
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DOI of Published Version
https://doi.org/10.3390/w18090994
