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An Early Warning Model for Forced Displacement

Geraldine Henningsen

TL;DR

The paper tackles the challenge of translating conflict forecasts into actionable displacement risk signals by proposing a global, threshold-based, gradient-boosting framework that leverages 95 country-level indicators and UNHCR nowcasting to predict two risk types at monthly horizons. It introduces a three-class, one-vs-one gradient boosting architecture with PCA-based dimensionality reduction, horizon-aware lagging, and probability calibration to deliver calibrated multiclass probabilities for significant flows and sudden increases. The key contributions include integrating multi-factor predictors, distinguishing between ongoing significant flows and sudden spikes, and providing monthly risk indices with 1-, 3-, and 6-month horizons that perform robustly for large displacement events. The approach offers humanitarian planners a quantitative, lead-time signal for anticipatory action while emphasizing its role as decision-support within a broader analytical framework and highlighting avenues for future enhancements such as finer geographic granularity and additional data sources.

Abstract

Monitoring tools for anticipatory action are increasingly gaining traction to improve the efficiency and timeliness of humanitarian responses. Whilst predictive models can now forecast conflicts with high accuracy, translating these predictions into potential forced displacement movements remains challenging because it is often unclear which precise events will trigger significant population movements. This paper presents a novel monitoring approach for refugee and asylum seeker flows that addresses this challenge. Using gradient boosting classification, we combine conflict forecasts with a comprehensive set of economic, political, and demographic variables to assess two distinct risks at the country of origin: the likelihood of significant displacement flows and the probability of sudden increases in these flows. The model generates country-specific monthly risk indices for these two events with prediction horizons of one, three, and six months. Our analysis shows high accuracy in predicting significant displacement flows and good accuracy in forecasting sudden increases in displacement--the latter being inherently more difficult to predict, given the complexity of displacement triggers. We achieve these results by including predictive factors beyond conflict, thereby demonstrating that forced displacement risks can be assessed through an integrated analysis of multiple country-level indicators. Whilst these risk indices provide valuable quantitative support for humanitarian planning, they should always be understood as decision-support tools within a broader analytical framework.

An Early Warning Model for Forced Displacement

TL;DR

The paper tackles the challenge of translating conflict forecasts into actionable displacement risk signals by proposing a global, threshold-based, gradient-boosting framework that leverages 95 country-level indicators and UNHCR nowcasting to predict two risk types at monthly horizons. It introduces a three-class, one-vs-one gradient boosting architecture with PCA-based dimensionality reduction, horizon-aware lagging, and probability calibration to deliver calibrated multiclass probabilities for significant flows and sudden increases. The key contributions include integrating multi-factor predictors, distinguishing between ongoing significant flows and sudden spikes, and providing monthly risk indices with 1-, 3-, and 6-month horizons that perform robustly for large displacement events. The approach offers humanitarian planners a quantitative, lead-time signal for anticipatory action while emphasizing its role as decision-support within a broader analytical framework and highlighting avenues for future enhancements such as finer geographic granularity and additional data sources.

Abstract

Monitoring tools for anticipatory action are increasingly gaining traction to improve the efficiency and timeliness of humanitarian responses. Whilst predictive models can now forecast conflicts with high accuracy, translating these predictions into potential forced displacement movements remains challenging because it is often unclear which precise events will trigger significant population movements. This paper presents a novel monitoring approach for refugee and asylum seeker flows that addresses this challenge. Using gradient boosting classification, we combine conflict forecasts with a comprehensive set of economic, political, and demographic variables to assess two distinct risks at the country of origin: the likelihood of significant displacement flows and the probability of sudden increases in these flows. The model generates country-specific monthly risk indices for these two events with prediction horizons of one, three, and six months. Our analysis shows high accuracy in predicting significant displacement flows and good accuracy in forecasting sudden increases in displacement--the latter being inherently more difficult to predict, given the complexity of displacement triggers. We achieve these results by including predictive factors beyond conflict, thereby demonstrating that forced displacement risks can be assessed through an integrated analysis of multiple country-level indicators. Whilst these risk indices provide valuable quantitative support for humanitarian planning, they should always be understood as decision-support tools within a broader analytical framework.
Paper Structure (16 sections, 3 equations, 6 figures, 5 tables)

This paper contains 16 sections, 3 equations, 6 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Literature overview
  3. Data
  4. Dataset Structure and Coverage
  5. Dependent Variables and Risk Classification
  6. Change Point Detection
  7. Feature variables
  8. Method
  9. Data Preprocessing and Dimensionality Reduction
  10. Model Architecture
  11. Model Estimation and Hyperparameter Optimization
  12. Probability Calibration
  13. Results and Discussion
  14. Metrics
  15. Predicted Probabilities
  16. ...and 1 more sections

Figures (6)

  • Figure 1: Changepoints in the time series of refugee flows for Afghanistan, Yemen, and Venezuela
  • Figure 2: Proportion of observations in each class at threshold 2000
  • Figure 3: Calibration plots by class and predictive horizon for threshold 2000
  • Figure 4: ROC curves for all three classes for each predictive horizon (1 month, 3 months, 6 months) - 2000 and 25000 threshold
  • Figure 5: Kernel density plots class one against other classes by threshold and ptredictive horizon
  • ...and 1 more figures