Turbulence along the Runway Glide Path: The Invisible Hazard Assessment Based on a Wind Tunnel Study and Interpretable TPE-Optimized KTBoost Approach

Abstract

Aircraft landings can be dangerous near airport runways due to wind variability. As a result, an aircraft could potentially miss an approach or divert off its flight path. In this study, turbulence intensity along the runway glide path was investigated using a scaled-down model of Hong Kong International Airport (HKIA) and the complex terrain nearby built in a TJ-3 atmospheric boundary layer wind tunnel. Different factors, including the effect of terrain, distance from the runway threshold, assigned approach runway, wind direction, and wind speed, were taken into consideration. Next, based on the experimental results, we trained and tested a novel tree-structured Parzen estimator (TPE)-optimized kernel and tree-boosting (KTBoost) model. The results obtained by the TPE-optimized KTBoost model outperformed other advanced machine learning models in terms of MAE (0.83), MSE (1.44), RMSE (1.20), and R2 (0.89). The permutation-based importance analysis using the TPE-optimized KTBoost model also revealed that the top three factors that contributed to the high turbulence intensity were the effect of terrain, distance from the runway threshold, and wind direction. The presence of terrain, the shorter distance from the runway, and the wind direction from 90 degrees to 165 degrees all contributed to high turbulence intensity.

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

“…In this research, we implemented six ML algorithms for predicting Anti-MRSA peptides: KTBoost, 36 SVM, 37 CatBoost, 38 Hist-GBoost, 39 CDF, 40 and XGBoost-RFC. 41 The implementation of all these classifiers was based on the Scikit-learn, 42 gcforest, 40 and KTBoost 43 packages.…”