SPE-SHAP: Self-paced ensemble with Shapley additive explanation for the analysis of aviation turbulence triggered by wind shear events

Abstract

Aviation safety is critically challenged by significant turbulence, often triggered by wind shear events, which jeopardize aircraft controllability and may cause structural damage to the fuselage. Current predictive models, primarily based on Pilot Reports (PIREPs), suffer from data imbalance, undermining their reliability and accuracy. This research addresses the need for a robust approach to accurately estimate and classify aviation turbulence events. We propose a novel framework, the Self-Paced Ensemble (SPE), integrated with Shapley Additive Explanation (SHAP) analysis. This framework utilizes tree-based classifiers such as Gradient Boosting Decision Trees (GBDT), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Extra Trees (ET) to enhance predictive accuracy and reliability. The SPE model, particularly when combined with XGBoost, demonstrated superior performance with a recall of 70.16 %, specificity of 74.75 %, G-Mean of 73.67 %, Matthews correlation coefficient (MCC) of 0.352, and area under the Receiver Operating Characteristic (ROC) curve of 0.726. Comparatively, the SPE model with RF achieved a recall of 70.05 %, specificity of 73.89 %, G-Mean of 72.57 %, MCC of 0.347, and AU-ROC of 0.721. Post-hoc Wilcoxon signed-rank tests further validated these results. SHAP analysis identified wind shear altitude, magnitude, and causes as the most influential factors affecting significant aviation turbulence prediction. The SPE-SHAP framework not only significantly improves the prediction of aviation turbulence but also enhances model transparency by elucidating key contributing factors. These insights can inform updates to aircraft operation protocols and enhance flight training, thereby increasing safety during wind shear events.