AI for Anticipatory Action: Moving Beyond Climate Forecasting
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
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| _version_ | 1866916284269068288 |
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| author | Huynh, Benjamin Q. Kiang, Mathew V. |
| author_facet | Huynh, Benjamin Q. Kiang, Mathew V. |
| contents | Disaster response agencies have been shifting from a paradigm of climate forecasting towards one of anticipatory action: assessing not just what the climate will be, but how it will impact specific populations, thereby enabling proactive response and resource allocation. Machine learning models are becoming exceptionally powerful at climate forecasting, but methodological gaps remain in terms of facilitating anticipatory action. Here we provide an overview of anticipatory action, review relevant applications of machine learning, identify common challenges, and highlight areas where machine learning can uniquely contribute to advancing disaster response for populations most vulnerable to climate change. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_15727 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AI for Anticipatory Action: Moving Beyond Climate Forecasting Huynh, Benjamin Q. Kiang, Mathew V. Machine Learning Artificial Intelligence Applications Disaster response agencies have been shifting from a paradigm of climate forecasting towards one of anticipatory action: assessing not just what the climate will be, but how it will impact specific populations, thereby enabling proactive response and resource allocation. Machine learning models are becoming exceptionally powerful at climate forecasting, but methodological gaps remain in terms of facilitating anticipatory action. Here we provide an overview of anticipatory action, review relevant applications of machine learning, identify common challenges, and highlight areas where machine learning can uniquely contribute to advancing disaster response for populations most vulnerable to climate change. |
| title | AI for Anticipatory Action: Moving Beyond Climate Forecasting |
| topic | Machine Learning Artificial Intelligence Applications |
| url | https://arxiv.org/abs/2307.15727 |