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  • Review Article
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Deep learning for water quality

Nature Water volume 2, pages 228–241 (2024)Cite this article

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Abstract

Understanding and predicting the quality of inland waters are challenging, particularly in the context of intensifying climate extremes expected in the future. These challenges arise partly due to complex processes that regulate water quality, and arduous and expensive data collection that exacerbate the issue of data scarcity. Traditional process-based and statistical models often fall short in predicting water quality. In this Review, we posit that deep learning represents an underutilized yet promising approach that can unravel intricate structures and relationships in high-dimensional data. We demonstrate that deep learning methods can help address data scarcity by filling temporal and spatial gaps and aid in formulating and testing hypotheses via identifying influential drivers of water quality. This Review highlights the strengths and limitations of deep learning methods relative to traditional approaches, and underscores its potential as an emerging and indispensable approach in overcoming challenges and discovering new knowledge in water-quality sciences.

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Fig. 1: Gauges through ages and across a few representative water-quality variables.
Fig. 2: The use of a DL model for spatio-temporal water-quality gap filling.
Fig. 3: Global maps of groundwater arsenic.
Fig. 4: A conceptual diagram showing the ideas and approaches from black boxes to glass boxes towards robust model performance and knowledge discovery.

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Data availability

Streamflow data (Fig. 1a) from the Global Streamflow Indices and Metadata Archive (GSIM) were compiled from repositories at https://doi.org/10.1594/PANGAEA.887477 and https://doi.org/10.1594/PANGAEA.887470. Water-quality data (Fig. 1b) from the Global River Water Quality Archive (GRQA) were downloaded from https://doi.org/10.5281/zenodo.7056647.

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Acknowledgements

W.Z. was supported by the National Natural Science Foundation of China (52121006) and by the Barry and Shirley Isett Professorship (to L.L.) at Penn State University. L.L. was supported by the US National Science Foundation via the Critical Zone Collaborative Network (EAR-2012123 and EAR-2012669), Frontier Research in Earth Sciences (EAR-2121621), Signals in Soils (EAR-2034214), and US Department of Energy Environmental System Science (DE-SC0020146). J.P. was supported by Swiss Agency for Development and Cooperation (SDC) (WABES project, 7F-09963.02.01). This paper has been reviewed in accordance with the US Environmental Protection Agency’s peer and administrative review policies and approved for publication. Mention of trade names or commercial products does not constitute endorsement or recommendation for use. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement or recommendation for use by the US Government. Statements in this publication reflect the authors’ professional views and opinions and should not be construed to represent any determination or policy of the US Environmental Protection Agency.

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Authors and Affiliations

  1. The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China

    Wei Zhi

  2. Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA

    Wei Zhi & Li Li

  3. US Geological Survey, Reston, VA, USA

    Alison P. Appling

  4. Office of Research and Development, US Environmental Protection Agency, Cincinnati, OH, USA

    Heather E. Golden

  5. Department of Water Resources and Drinking Water, Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Dübendorf, Switzerland

    Joel Podgorski

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  5. Li Li
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Contributions

W.Z. and L.L. conceived the idea for the review paper and wrote the first draft. A.P.A., H.E.G. and J.P. provided content for multiple sections and edited multiple versions of the paper. L.L. finalized the paper.

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Correspondence to Li Li.

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Zhi, W., Appling, A.P., Golden, H.E. et al. Deep learning for water quality. Nat Water 2, 228–241 (2024). https://doi.org/10.1038/s44221-024-00202-z

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