Understanding the Social and Economic Factors Affecting Adverse Events in an Active Theater of War: A Neural Network Approach

ÇAKIT E. , Karwowski W.

International Conference on Applied Human Factors and Ergonomics, Amerika Birleşik Devletleri, 17 - 21 Temmuz 2017, ss.215-223 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1007/978-3-319-60747-4_20
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.215-223


This study focused on the application of artificial neural networks (ANNs) to model the effect of infrastructure development projects on terrorism security events in Afghanistan. The dataset include adverse events and infrastructure aid activity in Afghanistan from 2001 to 2010. Several ANN models were generated and investigated for Afghanistan and its seven regions. In addition to a soft-computing approach, a multiple linear regression (MLR) analysis was also performed to evaluate whether or not the ANN approach showed superior predictive performance compared to a classical statistical approach. According to the performance comparison, the developed ANN model provided better prediction accuracy with respect to the MLR approach. The results obtained from this analysis demonstrate that ANNs can predict the occurrence of adverse events according to economic infrastructure aid activity data.