Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge.
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| Title: | Physically-constrained evapotranspiration models with machine learning parameterization outperform pure machine learning: Critical role of domain knowledge. |
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| Authors: | Kim Y; Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, Canada.; Division of Hydrologic Sciences, Desert Research Institute, Las Vegas, Nevada, United States of America., Garcia M; Estación Experimental de Zonas Áridas. Consejo Superior de Investigaciones Científicas (EEZA-CSIC), Almería, Spain., Andrew Black T; Faculty of Land and Food Systems, University of British Columbia, Vancouver, Canada., Johnson MS; Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver, Canada.; Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia, Vancouver, Canada. |
| Source: | PloS one [PLoS One] 2025 Jul 23; Vol. 20 (7), pp. e0328798. Date of Electronic Publication: 2025 Jul 23 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0328798 |
