Forecasting Internally Displaced Population Migration Patterns in Syria and Yemen

TL;DR

This paper demonstrates that machine learning models can accurately forecast monthly internal displacement flows in Syria and Yemen using publicly available data, aiding proactive humanitarian aid planning.

Contribution

It introduces a novel approach to predict IDP migration patterns one month in advance with publicly available data, improving aid response strategies.

Findings

Machine learning models outperform baseline persistence models in forecasting IDP flows.

Forecasting accuracy is sufficient for proactive aid allocation.

Publicly available data sources are effective for migration prediction.

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.