{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/forecasting-internally-displaced-population","title":"Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen","arxiv_id":"1806.08819","date":"2018-06-22","proceeding":null,"authors":["Benjamin Q. Huynh","Sanjay Basu"],"abstract":"Armed conflict has led to an unprecedented number of internally displaced\npersons (IDPs) - individuals who are forced out of their homes but remain\nwithin their country. IDPs often urgently require shelter, food, and\nhealthcare, yet prediction of when large fluxes of IDPs will cross into an area\nremains a major challenge for aid delivery organizations. Accurate forecasting\nof IDP migration would empower humanitarian aid groups to more effectively\nallocate resources during conflicts. We show that monthly flow of IDPs from\nprovince to province in both Syria and Yemen can be accurately forecasted one\nmonth in advance, using publicly available data. We model monthly IDP flow\nusing data on food price, fuel price, wage, geospatial, and news data. We find\nthat machine learning approaches can more accurately forecast migration trends\nthan baseline persistence models. Our findings thus potentially enable\nproactive aid allocation for IDPs in anticipation of forecasted arrivals.","url_abs":"http://arxiv.org/abs/1806.08819v1","url_pdf":"http://arxiv.org/pdf/1806.08819v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"forecasting-internally-displaced-population","repo_url":"https://github.com/benhuynh/migrationPatterns","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"humanitarian","task_name":"Humanitarian"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}