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Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream

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Abstract

Water quality monitoring for urban watersheds is critical to identify the negative urbanization impacts. This study sought to identify a successful predictive machine learning model with minimal parameters from easy-to-deploy, low-cost sensors to create a monitoring system for the urban stream network, Hunnicutt Creek, in Clemson, SC, USA. A multiple linear regression model was compared to machine learning algorithms k-nearest neighbor, decision tree, random forest, and gradient boosting. These algorithms were evaluated to understand which best predicted dissolved oxygen (DO) from water temperature, conductivity, turbidity, and water level change at four locations along the urban stream. The random forest algorithm had the highest performance in predicting DO for all four sites, with Nash–Sutcliffe model efficiency coefficient (NSE) scores > 0.9 at three sites and > 0.598 at the fourth site. The random forest model was further examined using explainable artificial intelligence (XAI) and found that temperature influenced the DO predictions for three of the four sites, but there were different water quality interactions depending on site location. Calculating the land cover type in each site’s sub-watershed revealed that different amounts of impervious surface and vegetation influenced water quality and the resulting DO predictions. Overall, machine learning combined with land cover data helps decision-makers better understand the nuances of urban watersheds and the relationships between urban land cover and water quality.

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Acknowledgements

Gratitude is extended to the Intelligent Rivers® Hunnicutt Creek project for access to their Hunnicutt Creek sensor network data used in this study. We would like to thank the Editor and Reviewers for their support in this work and for constructive comments that enhanced the quality of this manuscript. Special thanks goes to Reviewer #3 for their insightful comments and discussion with regard to the direction of possible future research pathways.

Funding

This work was supported by the Clemson University Hunnicutt Creek Project and the Higher Education Challenge (HEC) grants program [grant no. 2020–70003-32310] from the USDA National Institute of Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the US Department of Agriculture.

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Authors and Affiliations

  1. Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC, 29634, USA

    Madeleine M. Bolick, Christopher J. Post & Elena A. Mikhailova

  2. Department of Civil and Environmental Engineering & Earth Sciences, Clemson University, Clemson, SC, 29634, USA

    Mohannad-Zeyad Naser

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Contributions

Madeleine Bolick, Christopher Post, and M.Z. Naser contributed to the study conception and design. Data collection was performed by Madeleine Bolick and Christopher Post. Material preparation and analysis were performed by Madeleine Bolick. The first draft of the manuscript was written by Madeleine Bolick, and Christopher Post, MZ. Naser, and Elena Mikhailova edited previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Madeleine M. Bolick.

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Bolick, M.M., Post, C.J., Naser, MZ. et al. Comparison of machine learning algorithms to predict dissolved oxygen in an urban stream. Environ Sci Pollut Res 30, 78075–78096 (2023). https://doi.org/10.1007/s11356-023-27481-5

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