Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality
February 11, 2021
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.
Citation Information
| Publication Year | 2021 |
|---|---|
| Title | Using machine learning to develop a predictive understanding of the impacts of extreme water cycle perturbations on river water quality |
| DOI | 10.2172/1769795 |
| Authors | Charuleka Varadharajan, Vipin Kumar, Jared Willard, Jacob Aaron Zwart, Jeffrey Michael Sadler, Helen Weierbach, Talita Perciano, Juliane Mueller, Valerie Hendrix, Danielle Christianson |
| Publication Type | Report |
| Publication Subtype | Federal Government Series |
| Series Title | Technical Report |
| Index ID | 70248718 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | WMA - Integrated Information Dissemination Division |