Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality
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Abstract
This whitepaper addresses to two focal areas – (3) Insight gleaned from complex data using Artificial Intelligence (AI), and other advanced techniques (primary), and (2) Predictive modeling through the use of AI techniques and AI-derived model components (secondary). This topic is directly relevant to four DOE Earth and Environmental Systems Science Division Grand Challenges: integrated water cycle, biogeochemistry, drivers and responses in the Earth system, and data-model integration.
Suggested Citation
Varadharajan, C., Kumar, V., Willard, J., Zwart, J.A., Sadler, J.M., Weierbach, H., Perciano, T., Mueller, J., Hendrix, V., and Christianson, D., 2021, Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality: Technical Report, 5 p., https://doi.org/10.2172/1769795.
| Publication type | Report |
|---|---|
| Publication Subtype | Federal Government Series |
| Title | Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality |
| Series title | Technical Report |
| DOI | 10.2172/1769795 |
| Year Published | 2021 |
| Language | English |
| Publisher | Department of Energy |
| Contributing office(s) | WMA - Integrated Information Dissemination Division |
| Description | 5 p. |