Leveraging unsupervised machine learning to examine women's vulnerability to climate change
We provide an application of machine learning to identify the distributional consequences of climate change in Malawi. We compare climate impact estimates based on drought indicators established objectively from the k-means algorithm to more traditional measures. Young women affected by drought were...
| Autores principales: | , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/145032 |
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