Predicting refugee and internally displaced persons (IDP) arrivals is of critical interest in humanitarian emergencies since field operations teams must prepare in advance for these arrivals. The better prepared they are the better assistance teams can provide in the form of food, shelter, and other protection-related services, as well as for contingency planning purposes.
Project Jetson is a collaborative initiative led by the innovation unit of the United Nations High Commissioner for Refugees (UNHCR) to address this challenge. Project Jetson aims to provide a dashboard early warning system that displays monthly predicted IDP arrivals for 18 regions in Somalia. The project aggregates data on potential causes of displacement from internal and external sources, including data on conflict events and fatalities, wages and commodity prices, climate-related anomalies, and historical displacement flows.
The team initially tested different machine learning models for making predictions, and presented the results in a dashboard that illustrated historical arrivals alongside predictions for the three top-performing models. In the second iteration, UN Global Pulse worked with Project Jetson to extend the dashboard. Specifically, UN Global Pulse worked to standardize the collection of input data, extend the prediction horizon from one to three months, and build a second, internal dashboard that allows users to compare predictions across all models, explore the input data, and assess the performance of the algorithms.
The dashboard is being refined to incorporate additional input from users. Next steps include improving the predictive performance of the models, streamlining the process of incorporating new data and updating predictions, and making forecasts at a more granular, sub-regional level.
