PRIMJENA PREDIKATIVNE ANALITIKE U IDENTIFIKACIJI BEZBJEDNOSNIH RIZIKA VAZDUHOPLOVSTVA
APPLICATION OF PREDICTIVE ANALYTICS IN IDENTIFICATION OF AVIATION SECURITY RISKS

Summary/Abstract: This paper explores the application of predictive analytics in the identification and prevention of security risks in aviation. Through analysis of contemporary machine learning methods, statistical models, and algorithms for big data processing, the paper demonstrates how predictive analytics can significantly improve existing security systems. The research focuses on developing an integrated model that combines data on technical maintenance, flight operational parameters, meteorological conditions, and human factors. The methodology encompasses quantitative analysis of historical data on incidents and accidents, as well as qualitative analysis of existing security protocols. Results show that implementation of predictive models can reduce incident risk by 35-40%, while simultaneously reducing operational costs by 20-25%. The paper›s conclusion emphasizes the need for further development of integrated systems that combine traditional risk analysis methods with advanced predictive analytics algorithms, with special focus on ethical and legal aspects of using artificial intelligence in aviation security.

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