Knowledge-Guided Machine Learning (KGML) Platform to Predict Integrated Water Cycle and Associated extremes
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Univ. of California, Davis, CA (United States)
- Univ. of Arizona, Tucson, AZ (United States)
- Upstream Tech, New Brighton, MN (United States)
- Northern Arizona Univ., Flagstaff, AZ (United States)
- Johannes Kepler Univ. Linz (Austria)
- Karlsruhe Inst. of Technology (KIT) (Germany)
- Univ. of Illinois at Urbana-Champaign, IL (United States)
Focal Area(s): Predictive modeling through the use of AI techniques and insight gleaned from complex data (both observed and simulated). Science Challenge: Although advanced predictive capabilities of the water cycle are critical to address environmental needs and develop sustainable solutions for energy demands, there is no robust framework, to say the least, that seamlessly integrates local to intermediate to global scales and a gamut of biogeophysical information to enhance understanding of the integrated water cycle and its associated extremes.
- 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:
- 1769733
- Report Number(s):
- AI4ESP--1037
- Country of Publication:
- United States
- Language:
- English
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