Using Machine Learning to Develop a Predictive Understanding of the Impacts of Extreme Water Cycle Perturbations on River Water Quality
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of Minnesota, Minneapolis, MN (United States)
- US Geological Survey, Denver, CO (United States)
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.
- Research Organization:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Univ. of Minnesota, Minneapolis, MN (United States); US Geological Survey, Denver, CO (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI ID:
- 1769795
- Report Number(s):
- AI4ESP1135
- Country of Publication:
- United States
- Language:
- English
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