Title

Investigating The Relationship Between Adverse Events And Infrastructure Development In An Active War Theater Using Soft Computing Techniques

Keywords

Adaptive neuro-fuzzy inference systems (ANFIS); Adverse events; Artificial neural networks (ANNs); Fuzzy inference system (FIS); Infrastructure development; Soft computing

Abstract

The purpose of this paper is to investigate the relationship between adverse events and infrastructure development investments in an active war theater by using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) where the accuracy of the predictions is directly beneficial from an economic and humanistic point of view. Fourteen developmental and economic improvement projects were selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded or hijacked, and the total number of adverse events has been estimated. The results obtained from analysis and testing demonstrate that ANN, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic project data. When the model accuracy was calculated based on the mean absolute percentage error (MAPE) for each of the models, ANN had better predictive accuracy than FIS and ANFIS models, as demonstrated by experimental results. For the purpose of allocating resources and developing regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater, with emphasis on predicting the occurrence of events. We conclude that the importance of infrastructure development projects varied based on the specific regions and time period.

Publication Date

1-1-2014

Publication Title

Applied Soft Computing Journal

Volume

25

Number of Pages

204-214

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.asoc.2014.09.028

Socpus ID

84907972397 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84907972397

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