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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2410.21511 |
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| _version_ | 1866915002470891520 |
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| author | Goldani, Mahdi |
| author_facet | Goldani, Mahdi |
| contents | 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 absolute percentage error of less than 10% across all countries. The results indicate that Bahrain and Saudi Arabia are likely to experience improvements in their safety and security indexes. At the same time, Kuwait and Oman may face challenges in maintaining their current levels of security. The findings suggest that economic diversification, environmental sustainability, and social stability are critical for ensuring long-term security in the region. This study provides valuable insights for policymakers in designing proactive strategies to address emerging security threats. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21511 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safety and Security Dynamics in Gulf Cooperation Council (GCC) Countries: A Machine Learning Approach to Forecasting Security Trends Goldani, Mahdi General Economics Economics 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 absolute percentage error of less than 10% across all countries. The results indicate that Bahrain and Saudi Arabia are likely to experience improvements in their safety and security indexes. At the same time, Kuwait and Oman may face challenges in maintaining their current levels of security. The findings suggest that economic diversification, environmental sustainability, and social stability are critical for ensuring long-term security in the region. This study provides valuable insights for policymakers in designing proactive strategies to address emerging security threats. |
| title | Safety and Security Dynamics in Gulf Cooperation Council (GCC) Countries: A Machine Learning Approach to Forecasting Security Trends |
| topic | General Economics Economics |
| url | https://arxiv.org/abs/2410.21511 |