Multivariate random forest prediction of poverty and malnutrition prevalence
Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer...
| Autores principales: | , , , , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Public Library of Science
2021
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/142847 |
| _version_ | 1855516910309867520 |
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| author | Browne, Chris Matteson, David S. McBride, Linden Hu, Leiqiu Liu, Yanyan Sun, Ying Wen, Jiaming Barrett, Christopher B. |
| author_browse | Barrett, Christopher B. Browne, Chris Hu, Leiqiu Liu, Yanyan Matteson, David S. McBride, Linden Sun, Ying Wen, Jiaming |
| author_facet | Browne, Chris Matteson, David S. McBride, Linden Hu, Leiqiu Liu, Yanyan Sun, Ying Wen, Jiaming Barrett, Christopher B. |
| author_sort | Browne, Chris |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods. |
| format | Journal Article |
| id | CGSpace142847 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1428472025-02-24T06:45:19Z Multivariate random forest prediction of poverty and malnutrition prevalence Browne, Chris Matteson, David S. McBride, Linden Hu, Leiqiu Liu, Yanyan Sun, Ying Wen, Jiaming Barrett, Christopher B. models data forecasting surveys remote sensing technology machine learning capacity development malnutrition nutrition poverty Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer learning methods whose underlying mechanics may be challenging to interpret. We demonstrate how interpretable random forest models can produce estimates of a set of (potentially correlated) malnutrition and poverty prevalence measures using free, open access, regularly updated, georeferenced data. We demonstrate two use cases: contemporaneous prediction, which might be used for poverty mapping, geographic targeting, or monitoring and evaluation tasks, and a sequential nowcasting task that can inform early warning systems. Applied to data from 11 low and lower-middle income countries, we find predictive accuracy broadly comparable for both tasks to prior studies that use proprietary data and/or deep or transfer learning methods. 2021-09-14 2024-05-22T12:11:10Z 2024-05-22T12:11:10Z Journal Article https://hdl.handle.net/10568/142847 en https://doi.org/10.1002/aepp.13175 Open Access Public Library of Science Browne, Chris; Matteson, David S.; McBride, Linden; Hu, Leiqiu; Liu, Yanyan; Sun, Ying; Wen, Jiaming; Barrett, Christopher B. 2021. Multivariate random forest prediction of poverty and malnutrition prevalence. PLoS ONE 16(9): e0255519 https://doi.org/10.1371/journal.pone.0255519 |
| spellingShingle | models data forecasting surveys remote sensing technology machine learning capacity development malnutrition nutrition poverty Browne, Chris Matteson, David S. McBride, Linden Hu, Leiqiu Liu, Yanyan Sun, Ying Wen, Jiaming Barrett, Christopher B. Multivariate random forest prediction of poverty and malnutrition prevalence |
| title | Multivariate random forest prediction of poverty and malnutrition prevalence |
| title_full | Multivariate random forest prediction of poverty and malnutrition prevalence |
| title_fullStr | Multivariate random forest prediction of poverty and malnutrition prevalence |
| title_full_unstemmed | Multivariate random forest prediction of poverty and malnutrition prevalence |
| title_short | Multivariate random forest prediction of poverty and malnutrition prevalence |
| title_sort | multivariate random forest prediction of poverty and malnutrition prevalence |
| topic | models data forecasting surveys remote sensing technology machine learning capacity development malnutrition nutrition poverty |
| url | https://hdl.handle.net/10568/142847 |
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