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...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Wiley
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/145032 |
Similar Items: Leveraging unsupervised machine learning to examine women's vulnerability to climate change
- Drought vulnerability of central Sahel agrosystems: a modelling-approach based on magnitudes of changes and machine learning techniques
- Small reservoirs in the northern regions of Ghana and their vulnerability to drying
- A simplified measure of nutritional empowerment: Using machine learning to abbreviate the Women’s Empowerment in Nutrition Index (WENI)
- Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
- MusaDeepMosaic: Development of a machine learning genomic mosaic classifier tool.
- Digital soil mapping using machine learning model for Kapiti in Kenya