An independent evaluation of the Famine Early Warning Systems Network food security projections
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Accesso online: | |
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| _version_ | 1866912070048415744 |
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| author | Bertetti, Marco Agnolucci, Paolo Calzadilla, Alvaro Capra, Licia |
| author_facet | Bertetti, Marco Agnolucci, Paolo Calzadilla, Alvaro Capra, Licia |
| contents | Reports from the Famine Early Warning Systems Network (FEWSNET) serve as the benchmark for food security predictions which is crucial for stakeholders in planning interventions and support people in need. This paper assesses the predictive accuracy of FEWSNET's food security forecasting, by comparing predictions to the following ground truth assessments at the administrative boundaries-livelihood level, revealing an overall high accuracy of 78\% across diverse timeframes and locations. However, our analysis also shows significant variations in performance across distinct regions and prediction periods. Therefore, our analysis sheds light on strengths, weaknesses, and areas for improvement in the context of food security predictions. The insights derived from this study not only enhance our understanding of FEWSNET's capabilities but also emphasize the importance of continuous refinement in forecasting methodologies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09384 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An independent evaluation of the Famine Early Warning Systems Network food security projections Bertetti, Marco Agnolucci, Paolo Calzadilla, Alvaro Capra, Licia Numerical Analysis Reports from the Famine Early Warning Systems Network (FEWSNET) serve as the benchmark for food security predictions which is crucial for stakeholders in planning interventions and support people in need. This paper assesses the predictive accuracy of FEWSNET's food security forecasting, by comparing predictions to the following ground truth assessments at the administrative boundaries-livelihood level, revealing an overall high accuracy of 78\% across diverse timeframes and locations. However, our analysis also shows significant variations in performance across distinct regions and prediction periods. Therefore, our analysis sheds light on strengths, weaknesses, and areas for improvement in the context of food security predictions. The insights derived from this study not only enhance our understanding of FEWSNET's capabilities but also emphasize the importance of continuous refinement in forecasting methodologies. |
| title | An independent evaluation of the Famine Early Warning Systems Network food security projections |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2410.09384 |