Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

Charuleka Varadharajan, Alison P. Appling, Bhavna Arora, Danielle S. Christianson, Valerie C. Hendrix, Vipin Kumar, Aranildo R. Lima, Juliane Müller, Samantha Oliver, Mohammed Ombadi, Talita Perciano, Jeffrey M. Sadler, Helen Weierbach, Jared D. Willard, Zexuan Xu, Jacob Zwart

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.

Original languageEnglish (US)
Article numbere14565
JournalHydrological Processes
Volume36
Issue number4
DOIs
StatePublished - Apr 2022

Bibliographical note

Funding Information:
This paper is supported by the iNAIADS DOE Early Career Project and Watershed Function Science Focus Area funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award no. DE‐AC02‐05CH11231. Juliane Müller and Talita Perciano are supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory and the Office of Advanced Scientific Computing Research under U.S. Department of Energy Contract No. DE‐AC02‐05CH11231. Contributions of Samantha Oliver, Jacob Zwart, and Alison Appling were supported by the Water Mission Area of the U.S. Geological Survey. Vipin Kumar and Jared Willard were supported by NSF grant#1934721. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Funding Information:
This paper is supported by the iNAIADS DOE Early Career Project and Watershed Function Science Focus Area funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Award no. DE-AC02-05CH11231. Juliane M?ller and Talita Perciano are supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory and the Office of Advanced Scientific Computing Research under U.S. Department of Energy Contract No. DE-AC02-05CH11231. Contributions of Samantha?Oliver, Jacob Zwart, and Alison Appling were supported by the Water Mission Area of the U.S. Geological Survey. Vipin Kumar and Jared Willard were supported by NSF grant#1934721. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Publisher Copyright:
© 2022 The Authors. Hydrological Processes published by John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Keywords

  • analysis
  • machine learning
  • models
  • predictions
  • rivers
  • streams
  • water quality

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