Pushing the boundaries of anticipatory action using machine learning
Displacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities...
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Cambridge University Press
2025-01-01
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| Series: | Data & Policy |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632324924000889/type/journal_article |
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| author | Alexander Kjærum Bo S. Madsen |
| author_facet | Alexander Kjærum Bo S. Madsen |
| author_sort | Alexander Kjærum |
| collection | DOAJ |
| description | Displacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities before disaster strikes is crucial but anticipatory action is contingent on the ability to accurately forecast what will happen in the future. Forecasting and contingency planning are not new in the humanitarian sector, where scenario-building continues to be an exercise conducted in most humanitarian operations to strategically plan for coming events. However, the accuracy of these exercises remains limited. To address this challenge and work with the objective of providing the humanitarian sector with more accurate forecasts to enhance the protection of vulnerable groups, the Danish Refugee Council has already developed several machine learning models. The Anticipatory Humanitarian Action for Displacement uses machine learning to forecast displacement in subdistricts in the Liptako-Gourma region in Sahel, covering Burkina Faso, Mali, and Niger. The model is mainly built on data related to conflict, food insecurity, vegetation health, and the prevalence of underweight to forecast displacement. In this article, we will detail how the model works, the accuracy and limitations of the model, and how we are translating the forecasts into action by using them for anticipatory action in South Sudan and Burkina Faso, including concrete examples of activities that can be implemented ahead of displacement in the place of origin, along routes and in place of destination. |
| format | Article |
| id | doaj-art-91bfdaa40ab0485fb68d0d6ed889f4bb |
| institution | Kabale University |
| issn | 2632-3249 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data & Policy |
| spelling | doaj-art-91bfdaa40ab0485fb68d0d6ed889f4bb2025-01-17T08:42:37ZengCambridge University PressData & Policy2632-32492025-01-01710.1017/dap.2024.88Pushing the boundaries of anticipatory action using machine learningAlexander Kjærum0https://orcid.org/0009-0005-7848-4927Bo S. Madsen1Danish Refugee Council, Addis Ababa, EthiopiaDanish Refugee Council, Copenhagen, DenmarkDisplacement continues to increase at a global scale and is increasingly happening in complex, multicrisis settings, leading to more complex and deeper humanitarian needs. Humanitarian needs are therefore increasingly outgrowing the available humanitarian funding. Thus, responding to vulnerabilities before disaster strikes is crucial but anticipatory action is contingent on the ability to accurately forecast what will happen in the future. Forecasting and contingency planning are not new in the humanitarian sector, where scenario-building continues to be an exercise conducted in most humanitarian operations to strategically plan for coming events. However, the accuracy of these exercises remains limited. To address this challenge and work with the objective of providing the humanitarian sector with more accurate forecasts to enhance the protection of vulnerable groups, the Danish Refugee Council has already developed several machine learning models. The Anticipatory Humanitarian Action for Displacement uses machine learning to forecast displacement in subdistricts in the Liptako-Gourma region in Sahel, covering Burkina Faso, Mali, and Niger. The model is mainly built on data related to conflict, food insecurity, vegetation health, and the prevalence of underweight to forecast displacement. In this article, we will detail how the model works, the accuracy and limitations of the model, and how we are translating the forecasts into action by using them for anticipatory action in South Sudan and Burkina Faso, including concrete examples of activities that can be implemented ahead of displacement in the place of origin, along routes and in place of destination.https://www.cambridge.org/core/product/identifier/S2632324924000889/type/journal_articleanticipatory actiondisplacement forecastingmachine learningpredictive analyticshumanitarian response |
| spellingShingle | Alexander Kjærum Bo S. Madsen Pushing the boundaries of anticipatory action using machine learning Data & Policy anticipatory action displacement forecasting machine learning predictive analytics humanitarian response |
| title | Pushing the boundaries of anticipatory action using machine learning |
| title_full | Pushing the boundaries of anticipatory action using machine learning |
| title_fullStr | Pushing the boundaries of anticipatory action using machine learning |
| title_full_unstemmed | Pushing the boundaries of anticipatory action using machine learning |
| title_short | Pushing the boundaries of anticipatory action using machine learning |
| title_sort | pushing the boundaries of anticipatory action using machine learning |
| topic | anticipatory action displacement forecasting machine learning predictive analytics humanitarian response |
| url | https://www.cambridge.org/core/product/identifier/S2632324924000889/type/journal_article |
| work_keys_str_mv | AT alexanderkjærum pushingtheboundariesofanticipatoryactionusingmachinelearning AT bosmadsen pushingtheboundariesofanticipatoryactionusingmachinelearning |