Stacking Ensemble Learning with Regression Models for Predicting Damage from Terrorist Attacks

Abstract

Terrorist attacks can cause unexpectedly enormous damage to lives and property. To prevent and mitigate damage from terrorist activities, governments and related organizations must have suitable measures and efficient tools to cope with terrorist attacks. This work proposed a new method based on stacking ensemble learning and regression for predicting damage from terrorist attacks. First, two-layer stacking classifiers were developed and used to indicate if a terrorist attack can cause deaths, injuries, and property damage. For fatal and injury attacks, regression models were utilized to forecast the number of deaths and injuries, respectively. Consequently, the proposed method can efficiently classify casualty terrorist attacks with an average area under precision-recall curves (AUPR) of 0.958. Furthermore, the stacking model can predict property damage attacks with an average AUPR of 0.910. In comparison with existing methods, the proposed method precisely estimates the number of fatalities and injuries with the lowest mean absolute errors of 1.22 and 2.32 for fatal and injury attacks, respectively. According to the superior performance shown, the stacking ensemble models with regression can be utilized as an efficient tool to support emergency prevention and management of terrorist attacks.