Comparison of logistic regression and random forest algorithms for airport's runway assignment

Kanjanasurat, I.; Jungsuwadee, W.; Lasakul, A.; Benjangkaprasert, C.

doi:10.1088/1742-6596/2497/1/012016

Comparison of logistic regression and random forest algorithms for airport's runway assignment

Author(s)

Kanjanasurat, I.

Jungsuwadee, W.

Lasakul, A.

Benjangkaprasert, C.

Date Issued

January 1, 2023

Type

Conference Paper

DOI

10.1088/1742-6596/2497/1/012016

Abstract

Various automation systems are currently developed using machine learning techniques. It is used to predict and decide on numerous complex tasks in order to reduce the likelihood of human error. Logistic regression is one of the most widely employed machine learning (ML) algorithm. In this study, the accuracy of logistic regression was compared to that of random forest for the assignment of Suvarnabhumi Airport runways to arriving aircraft. The accuracy of the logistic regression model was determined to be 82%, while the accuracy of the random forest model was 77%. Logistic regression was found to be more precise for predicting the appropriate runway to assign to arriving aircraft.

Citation

Journal of Physics Conference Series, 2497(1), 2023