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., 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. |