Safety and Security Dynamics in Gulf Cooperation Council (GCC) Countries: A Machine Learning Approach to Forecasting Security Trends
Mahdi Goldani

TL;DR
This study employs machine learning to forecast security trends in GCC countries, highlighting key factors influencing regional safety and providing actionable insights for policymakers to enhance long-term security.
Contribution
It introduces a novel application of XGBoost with feature selection to predict security indices in GCC countries, offering high-accuracy forecasts and policy implications.
Findings
Bahrain and Saudi Arabia are projected to improve security.
Kuwait and Oman may face security challenges.
Economic and environmental factors are critical for regional security.
Abstract
The GCC region includes Saudi Arabia, UAE, Bahrain, Kuwait, Qatar, and Oman, which are of critical geopolitical and economic importance, being rich in oil and positioned along vital maritime routes. However, the region faces complex security challenges, ranging from traditional threats like interstate conflicts to nontraditional risks such as cyber-attacks, piracy, and environmental concerns. This study investigates the safety and security index for six GCC countries using machine learning techniques, specifically XGBoost, to forecast security trends for the next five years. Data from the Global Peace Index and World Bank development indicators were employed to construct the model. Key indicators related to economic, political, and environmental factors were selected using the Edit Distance on Real Sequence feature selection method. The model demonstrated high accuracy, with a mean…
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Taxonomy
TopicsTerrorism, Counterterrorism, and Political Violence
MethodsFeature Selection
