FAIR data infrastructure and tools for AI-assisted streamflow prediction
- Brookhaven National Lab. (BNL), Upton, NY (United States)
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Focal Area(s) Areas: We discuss how the integration of AI into Earth Science models can impact streamflow predictions at both the science and data levels. Doing so, we address cross-cutting needs related to the goal of making data FAIR (Findable, Accessible, Interoperable, and Re-usable [1]) for seamless use with Artificial Intelligence/Machine Learning (AI/ML) in Earth System Science at DOE. A novel idea is that AI/ML itself can help with the FAIR data goal and address issues in targeted areas e.g. missing data, data quality and reduction. In addition, the interpretability of results obtained with new AI methods is poised to impact broader scientific challenges in hydrology..
- Research Organization:
- Artificial Intelligence for Earth System Predictability (AI4ESP) Collaboration (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI ID:
- 1769710
- Report Number(s):
- AI4ESP--1105
- Country of Publication:
- United States
- Language:
- English
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OSTI ID:1769646