[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119139-en":3,"doc-seo-119139-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119139,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Predicting Food-Security Crises in the Horn of Africa Using Machine Learning","A machine-learning approach is developed to predict food-insecurity crises in the Horn of Africa, a region highly exposed to drought and long-running food shocks. The study presents an XGBoost model trained with more than 20 datasets and FEWS IPC current-situation estimates to forecast crises up to 12 months ahead. Results show effective capture of food-security dynamics up to 3 months (R2 > 0.6), with comparable skill to FEWS NET in pastoral and agro-pastoral regions, while crop-farming areas remain less accurately predicted. The model is positioned for integration into hybrid early warning systems.","RESEARCH ARTICLE  \n10.1029/2023EF004211  \nKey Points:  \n• A machine‐learning model is presented to predict food‐security crises in the Horn of Africa  \n• The model demonstrates high overall performance, and performs similarly to FEWS NET outlooks in the (agro‐) pastoral regions  \n• This study can be utilized to integrate machine learning into existing early warning systems, thereby creating hybrid solutions for the future  \nSupporting Information:  \nSupporting Information may be found in the online version of this article.  \nCorrespondence to:  \nT. Busker,  \n[tim.busker@vu.nl](tim.busker@vu.nl)  \n[Citation:](Citation:)  \nBusker, T., van den Hurk, B., de Moel, H., van den Homberg, M., van Straaten, C., Odongo, R. A., & Aerts, J. C. J. H. (2024) . Predicting food‐security crises in the Horn of Africa using machine learning. Earth's Future, 12, e2023EF004211. [https://doi](https://doi). org/10.1029/2023EF004211  \nReceived 19 OCT 2023 Accepted 25 JUN 2024  \nAuthor Contributions:  \nConceptualization: Tim Busker, Bart vanden Hurk, Hans de Moel, Jeroen  \nC. J. H. Aerts  \nData curation: Tim Busker, Rhoda  \nA. Odongo  \nFormal analysis: Tim Busker  \nMethodology: Tim Busker, Bart vanden Hurk, Hans de Moel, Marc vanden Homberg, Chiem van Straaten, Jeroen C. J. H. Aerts  \nProject administration: Tim Busker, Bart van den Hurk, Hans de Moel, Jeroen C. J. H. Aerts  \nSoftware: Tim Busker  \n© 2024. The Author(s) .  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nPredicting Food‐Security Crises in the Horn of Africa Using Machine Learning  \nTim Busker1 , Bart van den Hurk1,2, Hans de Moel1, Marc van den Homberg3,4 , Chiem van Straaten1,5, Rhoda A. Odongo1, and Jeroen C. J. H. Aerts1,2  \n1Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands, 2Deltares, Delft, The Netherlands, 3510, An Initiative of the Netherlands Red Cross, The Hague, The Netherlands, 4Faculty of Geo‐ Information Science and Earth Observation, University of Twente, Enschede, The Netherlands, 5Royal Netherlands Meteorological Institute, De Bilt, The Netherlands  \nAbstract In this study, we present a machine‐learning model capable of predicting food insecurity in the Horn of Africa, which is one of the most vulnerable regions worldwide. The region has frequently been affected by severe droughts and food crises over the last several decades, which will likely increase in future. Therefore, exploring novel methods of increasing early warning capabilities is of vital importance to reducing food‐ insecurity risk. We present a XGBoost machine‐learning model to predict food‐security crises up to 12 months in advance. We used >20 data sets and the FEWS IPC current‐situation estimates to train the machine‐learning model. Food‐security dynamics were captured effectively by the model up to 3 months in advance (R2 > 0.6) . Specifically, we predicted 20% of crisis onsets in pastoral regions (n = 96) and 20%–50% of crisis onsets inagro‐pastoral regions (n = 22) with a 3‐month lead time. We also compared our 8‐month model predictions to the 8‐month food‐security outlooks produced by FEWS NET. Over a relatively short test period (2019–2022), results suggest the performance of our predictions is similar to FEWS NET for agro‐pastoral and pastoral regions. However, our model is clearly less skilled in predicting food security for crop‐farming regions than FEWS NET. With the well‐established FEWS NET outlooks as a basis, this study highlights the potential for integrating machine‐learning methods into operational systems like FEWS NET.  \nPlain Language Summary In the face of increasing droughts and food crises, this study explored the use of machine learning to provide predictions of food crises in the Horn of Africa, up to 12 months in advance. We used an algorithm called “XGBoost,” which we fed wi","cbCaivXBZmS7MadD","https://ap.wps.com/l/cbCaivXBZmS7MadD","pdf",12276518,1,20,"English","en",105,"# Introduction\n# Model and Data\n## Training and Validation\n## Forecast Horizon (up to 12 months)\n# Results\n## Lead-time Performance (up to 3 months)\n## Comparison with FEWS NET\n## Regional Differences (pastoral, agro-pastoral, crop-farming)","[{\"question\":\"What machine-learning method is used to predict food-security crises in the Horn of Africa?\",\"answer\":\"An XGBoost machine-learning model is used to predict food-security crises based on multiple data inputs and FEWS IPC current-situation estimates.\"},{\"question\":\"How far in advance does the model aim to forecast food-security crises?\",\"answer\":\"The model is designed to predict crises up to 12 months in advance, with strong performance reported up to 3 months (R2 \\u003e 0.6).\"},{\"question\":\"How does the model’s performance compare with FEWS NET outlooks?\",\"answer\":\"Over a test period (2019–2022), predictions show similar performance to FEWS NET for agro-pastoral and pastoral regions, but the model is less skilled for crop-farming regions.\"}]","Predicting Food-Security Crises in the Horn of Africa Using Machine Learning | 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machine-learning method is used to predict food-security crises in the Horn of Africa?","Question",{"text":76,"@type":77},"An XGBoost machine-learning model is used to predict food-security crises based on multiple data inputs and FEWS IPC current-situation estimates.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How far in advance does the model aim to forecast food-security crises?",{"text":81,"@type":77},"The model is designed to predict crises up to 12 months in advance, with strong performance reported up to 3 months (R2 > 0.6).",{"name":83,"@type":74,"acceptedAnswer":84},"How does the model’s performance compare with FEWS NET outlooks?",{"text":85,"@type":77},"Over a test period (2019–2022), predictions show similar performance to FEWS NET for agro-pastoral and pastoral regions, but the model is less skilled for crop-farming 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