Advancing symbolic regression for earth science with a focus on evapotranspiration modeling
Abstract Artificial Intelligence (AI) assumes a pivotal role in Earth science, leveraging deep learning’s predictive capabilities. Despite its prevalence, the impact of AI on scientific discovery remains uncertain. In Earth sciences, the emphasis extends beyond mere accuracy, striving for groundbrea...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-12-01
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| Series: | npj Climate and Atmospheric Science |
| Online Access: | https://doi.org/10.1038/s41612-024-00861-5 |
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| Summary: | Abstract Artificial Intelligence (AI) assumes a pivotal role in Earth science, leveraging deep learning’s predictive capabilities. Despite its prevalence, the impact of AI on scientific discovery remains uncertain. In Earth sciences, the emphasis extends beyond mere accuracy, striving for groundbreaking discoveries with distinct physical properties essential for driving advancements through thorough analysis. Here, we introduce a novel knowledge-guided deep symbolic regression model (KG-DSR) incorporating prior knowledge of physical process interactions into the network. Using KG-DSR, we successfully derived the Penman-Monteith (PM) equation and generated a novel surface resistance parameterization. This new parameterization, grounded in fundamental cognitive principles, surpasses the conventional theory currently accepted in surface resistance parameterization. Importantly, the explicit physical processes generated by AI can generalize to future climate scenarios beyond the training data. Our results emphasize the role of AI in unraveling process intricacies and ushering in a new paradigm in tasks related to “AI for Land Surface Modeling.” |
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| ISSN: | 2397-3722 |