Çakıt, ErmanKarwowski, WaldemarHoffman, M13.07.20192019-07-1613.07.20192019-07-162018978-3-319-60747-4; 978-3-319-60746-72194-53572194-5365https://doi.org/10.1007/978-3-319-60747-4_20https://hdl.handle.net/20.500.12451/4740AHFE International Conference on Cross-Cultural Decision Making (CCDM) -- JUL 17-21, 2017 -- Los Angeles, CAWOS:000451449700020This 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.eninfo:eu-repo/semantics/closedAccessInfrastructure Aid ActivityAdverse EventsArtificial Neural NetworksMultiple Linear RegressionConference Object61021522310.1007/978-3-319-60747-4_20N/AWOS:000451449700020N/A