Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise
Document Type
Article
Publication Title
Engineering Proceedings
Abstract
Airfoil noise due to pressure fluctuations impacts the efficiency of aircraft and has created significant concern in the aerospace industry. Hence, there is a need to predict airfoil noise. This paper uses the airfoil dataset published by NASA (NACA 0012 airfoils) to predict the scaled sound pressure using five different input features. Diverse Random Forest and Gradient Boost Models are tested with five-fold cross-validation. Their performance is assessed based on mean-squared error, coefficient of determination, training time, and standard deviation. The results show that the Extremely Randomized Trees algorithm exhibits the most superior performance with the highest Coefficient of Determination.
DOI
10.3390/engproc2023059024
Publication Date
1-1-2023
Recommended Citation
Nadkarni, Shantaram B.; Vijay, G. S.; and Kamath, Raghavendra C., "Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise" (2023). Open Access archive. 8716.
https://impressions.manipal.edu/open-access-archive/8716
