Improving the accuracy of food security predictions by integrating conflict data

Abstract

Violence and armed conflicts have emerged as prominent factors driving food crises. However, the extent of their impact remains largely unexplored. This paper provides an in-depth analysis of the impact of violent conflicts on food security in Africa. We performed a comprehensive correlation analysis using data from the Famine Early Warning Systems Network (FEWSNET) and the Armed Conflict Location Event Data (ACLED). Our results show that using conflict data to train machine learning models leads to a 1.5% increase in accuracy compared to models that do not incorporate conflict-related information. The key contribution of this study is the quantitative analysis of the impact of conflicts on food security predictions.

Figures (9)

• Figure 1: Countries in blue are those for which FEWSNET generates regular assessments, hence included in this study.
• Figure 2: Source: fews.net
• Figure 3: Diagram of the data processing pipeline: in Step 1 we merge Administrative Boundaries with each published FS assessment; Step 2 aggregates ACLED data, counting conflicts by Administrative boundaries and event period; Step 3 combines all the data into a single table, covering assessments and count of conflicts over time and geographies
• Figure 4: Example of visualisation of Administrative Boundaries and FS assessment in Somalia from FEWSNET shape files.
• Figure 5: Visualisation of the final output dataset, result of combining Administrative Boundaries and FS assessment shape files published by FEWSNET (CS for February 2021)