Analysis of Aircraft Flight Safety Based on Random Forest Model and Its Broader Applications

Abstract

With the increasing importance of ensuring transportation safety, this study focuses on enhancing flight safety through data-driven methods. Given the limitations of traditional monitoring systems, which fail to identify the underlying causes of deviations in aircraft performance, this paper proposes an analysis framework leveraging flight data and random forest models. By pre-processing flight data and quantifying key influencing factors, the study identifies potential risks and establishes predictive models to minimize safety incidents. Furthermore, the versatility of the random forest model is highlighted, showing its applicability beyond aviation to domains such as healthcare, finance, and environmental monitoring, offering solutions for complex classification and regression problems.