基于机器学习和脆弱国家指数的全球恐怖袭击预测研究

恐怖袭击在全球范围内频发,针对恐怖袭击的预警及防控研究十分必要。利用2006-2016年脆弱国家指数及全球恐怖主义数据库(GTD),基于多种机器学习模型,对全球各国家遭受恐怖袭击的风险进行回归预测。结果表明,随机森林、K近邻及决策树模型表现最优,其拟合优度的确定系数R^2达到了0.75、0.74和0.67。随机森林预测结果总体符合实际情况,尤其在恐怖袭击高发的中东和中亚地区预测较为准确。根据特征重要性排序结果,安全机构、公共服务、人权法治和集团之间的矛盾对预测结果的刻画能力最强。

Study on Prediction of global terrorist attacks based on Machine Learning and Fragile States Index

Terrorist attacks occur frequently all over the world.Study on early warning,prevention and control of terrorist attacks is necessary.Methods of prediction of global terrorist attacks were studied using the data from Fragile States Index and Global Terrorism Database from 2006 to 2016,based on six kinds of Machine Learning Models.The results show that Random Forest,K-neighbors and Decision tree perform well,which has the highest R-squared as 0.75,0.74 and 0.67.The prediction results of Random Forests are generally in line with the actual situation,especially in the Middle East and Central Asia,where terrorist attacks occur frequently.According to the results of importance ranking of characteristics,Security Apparatus,Public Services,Human Rights and Rule of Law and Group Grievance have the strongest ability to portray prediction results.