Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866913827387342848 |
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| author | Xu, Hao Chen, Yuntian Zeng, Zhenzhong Li, Nina Li, Jian Zhang, Dongxiao |
| author_facet | Xu, Hao Chen, Yuntian Zeng, Zhenzhong Li, Nina Li, Jian Zhang, Dongxiao |
| contents | Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechanisms. To address this limitation, we propose an AI-driven knowledge discovery framework known as genetic algorithm-geographic weighted regression (GA-GWR). Our approach seeks to unveil the explicit equations that govern the intricate relationship between precipitation patterns and terrain characteristics in regions marked by complex terrain. Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns. These equations demonstrate remarkable accuracy when applied to precipitation data, outperforming conventional empirical models. Notably, our research reveals that the parameters within these equations are dynamic, adapting to evolving climate patterns. Ultimately, the unveiled equations have practical applications, particularly in fine-scale downscaling for precipitation predictions using low-resolution future climate data. This capability offers invaluable insights into the anticipated changes in precipitation patterns across diverse terrains under future climate scenarios, which enhances our ability to address the challenges posed by contemporary climate science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_15400 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights Xu, Hao Chen, Yuntian Zeng, Zhenzhong Li, Nina Li, Jian Zhang, Dongxiao Atmospheric and Oceanic Physics Machine Learning Geophysics Despite the remarkable strides made by AI-driven models in modern precipitation forecasting, these black-box models cannot inherently deepen the comprehension of underlying mechanisms. To address this limitation, we propose an AI-driven knowledge discovery framework known as genetic algorithm-geographic weighted regression (GA-GWR). Our approach seeks to unveil the explicit equations that govern the intricate relationship between precipitation patterns and terrain characteristics in regions marked by complex terrain. Through this AI-driven knowledge discovery, we uncover previously undisclosed explicit equations that shed light on the connection between terrain features and precipitation patterns. These equations demonstrate remarkable accuracy when applied to precipitation data, outperforming conventional empirical models. Notably, our research reveals that the parameters within these equations are dynamic, adapting to evolving climate patterns. Ultimately, the unveiled equations have practical applications, particularly in fine-scale downscaling for precipitation predictions using low-resolution future climate data. This capability offers invaluable insights into the anticipated changes in precipitation patterns across diverse terrains under future climate scenarios, which enhances our ability to address the challenges posed by contemporary climate science. |
| title | Interpretable AI-Driven Discovery of Terrain-Precipitation Relationships for Enhanced Climate Insights |
| topic | Atmospheric and Oceanic Physics Machine Learning Geophysics |
| url | https://arxiv.org/abs/2309.15400 |