Anticipating humanitarian emergencies with a high risk of conflict-induced displacement

Nicolas Rost and Michele Ronco

International Journal of Forecasting, 2026, vol. 42, issue 1, 138-157

Abstract: This exploratory study assesses the risk of future onset of large-scale, conflict-related internal displacement in countries facing humanitarian emergencies. We train a variety of machine learning models on near-real-time data, which we compare against a simple baseline model, to assess the risk, one and three months into the future, of whether at least 1,000 people per month will flee their homes due to conflict. Measures of past displacement, conflict, risk of humanitarian crises, humanitarian access, the severity of humanitarian crises, and free elections improve forecasting performance. Limitations include the fact that displacement onsets are rare and hard to predict, and limited data availability and quality. Still, the best random forest model flagged 24 of 26 cases of displacement onset three months into the future and identified a high-risk group of country-months with a 33 times higher probability of displacement onset than a low-risk group. Providing such monthly forecasts to humanitarian practitioners could help them prepare better for new displacement or even mitigate the human suffering caused by conflict.

Keywords: Displacement; Conflict; Humanitarian emergencies; Risk assessment; Machine learning (search for similar items in EconPapers)
Date: 2026
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207025000421
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:42:y:2026:i:1:p:138-157

DOI: 10.1016/j.ijforecast.2025.04.006

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().