Zero-shot Microclimate Prediction with Deep Learning
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866911749011144704 |
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| author | Deznabi, Iman Kumar, Peeyush Fiterau, Madalina |
| author_facet | Deznabi, Iman Kumar, Peeyush Fiterau, Madalina |
| contents | Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02665 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Zero-shot Microclimate Prediction with Deep Learning Deznabi, Iman Kumar, Peeyush Fiterau, Madalina Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics Weather station data is a valuable resource for climate prediction, however, its reliability can be limited in remote locations. To compound the issue, making local predictions often relies on sensor data that may not be accessible for a new, previously unmonitored location. In response to these challenges, we propose a novel zero-shot learning approach designed to forecast various climate measurements at new and unmonitored locations. Our method surpasses conventional weather forecasting techniques in predicting microclimate variables by leveraging knowledge extracted from other geographic locations. |
| title | Zero-shot Microclimate Prediction with Deep Learning |
| topic | Machine Learning Artificial Intelligence Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2401.02665 |