Abstract
Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.
Highlights
Terrorism is considered to be a major threat to society [1,2,3]
Based on a comparison between machine learning models applied to training datasets, we find that the random forest (RF) model obtained the highest area under the curve (AUC) (1) value, followed by the neural network (NNET) (0.980) and support vector machine (SVM) (0.976) models (Fig 3E)
We demonstrate a novel strategy based on large amounts of data that uses machine learning models that are relatively typical and robust, though not the most advanced, to simulate the risk of terrorist attacks in all regions worldwide, which proved to be reasonable
Summary
Terrorism is considered to be a major threat to society [1,2,3]. According to the Global Terrorism Database (GTD), more than 14800 terrorism events of different types occurred globally in 2015 alone[4], which caused the deaths of 38430 people and caused the world to panic[5,6,7]. Great efforts have been made to seek explanations of various issues related to terrorist threats[8, 9]. The prediction of the occurrence of a certain event is still a difficult task of great complexity and uncertainty[10, 11]. There are some regularity of terrorist attacks[18].
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