Conflict forecasting using remote sensing data: An application to the Syrian civil war

Daniel Racek, Paul W. Thurner, Brittany I. Davidson, Xiao Xiang Zhu, Göran Kauermann

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Conflict research is increasingly influenced by modern computational and statistical techniques. Combined with recent advances in the collection and public availability of new data sources, this allows for more accurate forecasting models in ever more fine-grained spatial areas. This paper demonstrates the utilization of remote sensing data as a potential solution to the lack of official data sources for conflict forecasting in crisis-ridden countries. We evaluate and quantify remote sensing data's differentiated impact on forecasting accuracy across fine-grained spatial grid cells using the Syrian civil war as a use case. It can be shown that conflict, particularly its onset, can be forecasted more accurately by employing publicly available remote sensing datasets. These results are consistent across a range of established statistical and machine learning models, which raises the hope to get closer to reliable early-warning systems for conflict prediction.

Original languageEnglish
Pages (from-to)373-391
Number of pages19
JournalInternational Journal of Forecasting
Volume40
Issue number1
DOIs
StatePublished - 1 Jan 2024

Keywords

  • Conflict prediction
  • Forecasting
  • Machine learning
  • Remote sensing
  • Satellite imagery
  • Statistical modeling
  • Syria

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