Three Principles to Use in Streamlining Water Quality Research through Data Uniformity

Shaughnessy, Andrew R.; Wen, Tao; Niu, Xianzeng; Brantley, Susan L.

doi:10.1021/acs.est.9b06406

Cited by 8 publications

(10 citation statements)

References 4 publications

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Section: Data Availability Integration Processing and Representation ...mentioning

confidence: 99%

“…In particular, water bodies with strong human influence are extremely difficult to model and upscale because predictor datasets on activities such as point source discharge, water withdrawals, and reservoir releases are limited or not easily reusable. Thus many observations are underutilized for ML despite large‐scale consolidation efforts such as the Water Quality Portal and GLORICH databases (Hartmann et al, 2014; Read et al, 2017), because it is labor‐intensive to harmonize and process data (Shaughnessy et al, 2019; Sprague et al, 2017). Recent efforts to make water data more broadly available and usable such as the U.S. Open Water Data Initiative and the California open water data system are essential for effective management and decision making (Blodgett et al, 2015; Cantor et al, 2021; Larsen et al, 2016).…”

Section: Considerations For the Use Of ML In Water Quality Modelsmentioning

confidence: 99%

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

Varadharajan

Appling

Arora

et al. 2022

Hydrological Processes

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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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Section: Data Availability Integration Processing and Representation ...mentioning

confidence: 99%

Section: Considerations For the Use Of ML In Water Quality Modelsmentioning

confidence: 99%

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

Varadharajan

Appling

Arora

et al. 2022

Hydrological Processes

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“…Even where geochemical data are already compiled and accessible in one place such as the National Water Quality Portal (USGS/EPA), the data are not harmonized, i.e., units, formats, analytical methods, detection limits, and other parameters are not presented consistently (e.g. SPRAGUE et al, 2016;SHAUGHNESSY et al, 2019). Apparently, data standards for agreed-upon units and measurement protocols have never emerged because i) communities have never felt enough need for or placed enough value on such standardization or ii) variations in protocols were simply necessary to answer the proposed research questions.…”

Section: Lack Of Best Practices Standards and Harmonization For Ltgmentioning

confidence: 99%

“…In this respect, soil and water quality data are often prized by decision-makers and the scientific community alike. On the other hand, if a decision-maker or scientist or member of the public must peruse multiple publications and web pages to pull together a dataset, or to adjust the units of a dataset (SHAUGHNESSY et al, 2019), the time needed for such discovery and harmonization can preclude time for deep analysis (LIU et al, 2020). Publicly accessible geochemical databases also accelerate collaboration among scientists and across disciplines and help to promote dialogue with the public (CHRISTENSEN et al, 2009;BRANTLEY et al, 2018).…”

Section: Lesson 1 Improving the Accessibility Of Geochemical Data Prmentioning

confidence: 99%

“…But none of the data repositories --specialized, targeted, and structured or generalized and unstructured --have required data harmonization: providers simply do not use the same method of sample preparation, analysis, nor reporting. For example, in the USGS National Water Information System, 32 different name-unit conventions are used for dissolved nitrate alone (SHAUGHNESSY et al, 2019). In some cases, monitoring networks and government agencies have imposed common standards across specific projects, but among the networks, agencies, and projects, no norms have been established.…”

Section: Institutions 2020mentioning

confidence: 99%

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A Vision for the Future Low-Temperature Geochemical Data-scape

Brantley¹,

Wen²,

Agarwal³

et al. 2020

Preprint

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Data sharing benefits the researcher, the scientific community, and most importantly, the public by enabling more impactful analysis of data and greater transparency in scientific research.However, like many other scientists, the low-temperature geochemistry (LTG) community has generally not developed protocols and standards for publishing, citing, and versioning datasets. This paper is the product of a group of LTG and data scientists convened to strategize about the future management of LTG data. The group observed that the current landscape of sites for LTG -the data-scape --is a "street bazaar" of data repositories. This was deemed appropriate because LTG scientists target many different scientific questions and produce data with different structures and volumes described by copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories. To enable this for sample-based data, samples should be registered with globally unique persistent identifiers. LTG scientists should be able to use both highly structured databases designed for specialized types of data or generalized, unstructured, and nontargeted data repositories. The group strategized that the overall data informatics paradigm should shift from "build data repository, data will come" to "publish data online, cybertools will find." In other words, the most important need within the growing and complex data-scape is for increasingly powerful tools for searching and cross-referencing data across the proliferating data repositories. This strategy requires increasing emphasis on data science and data management for LTG professionals and students.

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The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

Brantley

Wen

Agarwal

et al. 2021

Computers & Geosciences

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Three Principles to Use in Streamlining Water Quality Research through Data Uniformity

Cited by 8 publications

References 4 publications

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

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

A Vision for the Future Low-Temperature Geochemical Data-scape

The future low-temperature geochemical data-scape as envisioned by the U.S. geochemical community

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