AHEAD – Anticipatory Humanitarian Action for Displacement (Foresight Model)

Overview

The AHEAD (Anticipatory Humanitarian Action for Displacement) model is a machine learning system operated by the Danish Refugee Council (DRC) that forecasts forced displacement at the sub-district level in conflict-affected countries, including South Sudan, Somalia, and the Liptako-Gourma region in the Sahel covering Burkina Faso, Mali, and Niger. The system is designed to enable humanitarian actors to anticipate displacement crises and intervene before they escalate, shifting from reactive to anticipatory humanitarian response (DRC 2024).

The AHEAD model builds on DRC's earlier Foresight platform, which was originally established by DRC in partnership with IBM to scale up predictive analytics capacity in the humanitarian sector (Red Social Innovation 2020). The model is grounded in a theoretical framework focusing on the root causes and pre-disposing factors of displacement. It aggregates open-source data from 18 sources, including the World Bank development indicators, ACLED, UCDP, EMDAT, and UN agencies such as UNHCR, WFP, FAO, and IDMC, encompassing a total of 148 indicators. These indicators are organised into five categories: economy (e.g. unemployment, GDP, poverty), violence (e.g. civilian fatalities, number of conflict events), governance (e.g. corruption, access to public services, democracy), environment (e.g. food security, natural hazard events), and social/population (e.g. presence of vulnerable groups, urbanisation, population size). The categories were identified based on DRC field experience and standard fragility groupings used by the OECD and similar bodies (Red Social Innovation 2020).

Technically, the system employs an ensemble machine learning approach using gradient boosted trees and linear models to generate point forecasts. Model hyperparameters are determined through grid search. Each year-ahead forecast uses a separate model, training a set of ensemble models for different time horizons. Confidence intervals are generated using an empirical bootstrap method, with source error distributions produced through retrospective analysis. The model training data covers the period from 1995 onward (Red Social Innovation 2020). ACLED's conflict data are a critical input to the model, providing indicators on violent events, kidnappings, lootings, extremist attacks, and violence against civilians, which enable AHEAD to identify long-term conflict patterns and predict displacement trends. ACLED provides researcher-led data collection covering 244 countries and territories with at least seven years of historical data for each (ACLED 2024).

The model uses historical data from key displacement drivers to predict how many internally displaced persons (IDPs) there will be at a regional level, generating location-specific forecasts intended to improve operational responses. These forecasts are populated with open-source data on conflict, health, environment, food insecurity, numbers of IDPs, and income (DRC 2024). The model is hosted on an online platform where users can access the underlying data, view forecasts for different countries, and develop scenario-based forecasts of displacement (Red Social Innovation 2020). DRC provides public access to the AHEAD model through an interactive dashboard (DRC 2024).

In terms of deployment and documented outcomes, the Foresight model covers 26 displacement-producing countries. Of more than 150 forecasts produced across these countries, approximately 50 per cent have a margin of error of 10 per cent or below, and two-thirds have a margin of error of 15 per cent or below. The model can predict next year's displacement with an average margin of error as low as 6 per cent in its best-performing country. The model generally outperforms the accuracy of planning figures used in Humanitarian Response Plans (Red Social Innovation 2020). It has also been used by external partners to inform strategic planning, including in the Humanitarian Needs Overview (HNO) process for Central America and by OCHA CERF in funding allocation decision-making (Red Social Innovation 2020).

In May 2024, AHEAD triggered an early response in Akobo County, South Sudan, that prevented 2,800 people from being displaced, achieving a cost-effectiveness ratio of 6.60 euros saved for every 1 euro spent. This ratio is consistent with findings from a 2025 UN OCHA report concluding that every dollar invested in anticipatory action can yield up to seven dollars in avoided losses (ACLED 2024). The programme is funded by the European Civil Protection and Humanitarian Aid Operations (ECHO), the Swedish International Development Cooperation Agency (Sida), the Danish International Development Agency (Danida), and the Dutch Ministry of Foreign Affairs (DRC 2024).

Classification

Programme Details

AHEAD (Anticipatory Humanitarian Action for Displacement) is a machine learning forecasting system operated by the Danish Refugee Council that predicts forced displacement at the sub-district level in conflict-affected countries including South Sudan, Somalia, and the Liptako-Gourma region. It uses an ensemble of gradient boosted trees and linear models trained on 148 indicators from 18 open-source datasets to generate location-specific displacement forecasts, enabling anticipatory humanitarian response. Funded by ECHO, Sida, Danida, and the Dutch Ministry of Foreign Affairs.

Implementation Details

Risk & Oversight

Deployment & Outcomes

Sources

1. SRC-001-SSD-001 ACLED (2024) 'Helping the Danish Refugee Council anticipate and prevent forced displacement', ACLED. Available at: https://acleddata.com/helping-danish-refugee-council-anticipate-and-prevent-forced-displacement (Accessed: 23 March 2026).
   https://acleddata.com/helping-danish-refugee-council-anticipate-and-prevent-forced-displacement
2. SRC-004-SSD-001 Kjærum, A. and Madsen, B.S. (2025) 'Pushing the boundaries of anticipatory action using machine learning', Data & Policy, 7, e8. doi: 10.1017/dap.2024.88.
   https://doi.org/10.1017/dap.2024.88
3. SRC-002-SSD-001 Danish Refugee Council (2024) 'Anticipatory Humanitarian Action for Displacement (AHEAD) model', DRC. Available at: https://drc.ngo/what-we-do/innovation/anticipatory-action/ahead/ (Accessed: 23 March 2026).
   https://drc.ngo/what-we-do/innovation/anticipatory-action/ahead/
4. SRC-003-SSD-001 Red Social Innovation (2020) 'Foresight, using machine learning to predict refugees displacement', Red Social Innovation. Available at: https://red-social-innovation.com/en/solution/foresight-using-machine-learning-to-predict-refugees-displacement/ (Accessed: 23 March 2026).
   https://red-social-innovation.com/en/solution/foresight-using-machine-learning-to-predict-refugees-displacement/