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

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

doi:10.1002/hyp.14565

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

Journal Article · Sun Apr 24 00:00:00 EDT 2022 · Hydrological Processes

DOI:https://doi.org/10.1002/hyp.14565· OSTI ID:1864613

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Earth and Environmental Sciences Area Lawrence Berkeley National Laboratory Berkeley California 94010 USA
U.S. Geological Survey, Water Mission Area Reston Virginia USA
Computing Sciences Area Lawrence Berkeley National Laboratory Berkeley California 94720 USA
Department of Computer Science and Engineering University of Minnesota Minneapolis Minnesota USA
Aquatic Informatics Vancouver BC V6E 4M3 Canada

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: National Science Foundation (NSF); US Geological Survey; USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1864613

Alternate ID(s):: OSTI ID: 1865216
OSTI ID: 1869671

Journal Information:: Hydrological Processes, Journal Name: Hydrological Processes Journal Issue: 4 Vol. 36; ISSN 0885-6087

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United Kingdom

Language:: English

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Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations

Journal Article · Sat Nov 14 19:00:00 EST 2020 · Journal of Hydrology · OSTI ID:1842780

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

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