Title: Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen,
Abstract: Armed conflict has led to an unprecedented number of internally displaced
persons (IDPs) - individuals who are forced out of their homes but remain
within their country. IDPs often urgently require shelter, food, and
healthcare, yet prediction of when large fluxes of IDPs will cross into an area
remains a major challenge for aid delivery organizations. Accurate forecasting
of IDP migration would empower humanitarian aid groups to more effectively
allocate resources during conflicts. We show that monthly flow of IDPs from
province to province in both Syria and Yemen can be accurately forecasted one
month in advance, using publicly available data. We model monthly IDP flow
using data on food price, fuel price, wage, geospatial, and news data. We find
that machine learning approaches can more accurately forecast migration trends
than baseline persistence models. Our findings thus potentially enable
proactive aid allocation for IDPs in anticipation of forecasted arrivals.