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Conflict Forecasting Using Remote Sensing Data: an Application to the Syrian Civil War

INTERNATIONAL JOURNAL OF FORECASTING(2024)

Inst Stat | Ludwig Maximilians Univ Munchen | Univ Bath | Tech Univ Munich

Cited 1|Views41
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.
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Key words
Remote sensing,Satellite imagery,Conflict prediction,Forecasting,Machine learning,Statistical modeling,Syria
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要点】:本研究利用遥感数据提高了危机国家冲突预测的准确性,特别是针对叙利亚内战的案例,展示了遥感数据在冲突预警中的潜在应用价值。

方法】:研究通过评估和量化遥感数据在细粒度空间网格单元上对预测准确性的差异化影响,使用多种统计和机器学习模型进行分析。

实验】:在叙利亚内战案例中,实验采用了公开可获得的遥感数据集,结果显示使用这些数据集能够更准确地预测冲突,尤其是冲突的发生,结果在不同的模型中一致,为建立可靠的冲突预测早期预警系统提供了希望。