LAUSR

ABSTRACT Objectives: Armed conflict has contributed 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 IDPs will migrate to an area remains a major challenge for aid delivery organizations. We sought to develop an IDP migration forecasting framework that could empower humanitarian aid groups to more effectively allocate resources during conflicts. Methods: We modeled monthly IDP migration between provinces within Syria and within Yemen using data on food prices, fuel prices, wages, location, time, and conflict reports. We compared machine learning methods with baseline persistence methods of forecasting. Results: We found a machine learning approach that more accurately forecast migration trends than baseline persistence methods. A random forest model outperformed the best persistence model in terms of root mean square error of log migration by 26% and 17% for the Syria and Yemen datasets, respectively. Conclusions: Integrating diverse data sources into a machine learning model appears to improve IDP migration prediction. Further work should examine whether implementation of such models can enable proactive aid allocation for IDPs in anticipation of forecast arrivals.