基于Lasso-随机森林模型的航空器安全目标水平预测方法OACSCD

As aviation safety continuously improves,transportation accidents exhibit small-sample and low-proba-bility characteristics.Traditional prediction methods based on historical data struggle to characterize the evolution of aviation operational risks and refined safety management demands.To address prediction instability caused by in-sufficient accident samples,a method for calculating the target level of safety using a Lasso-random forest model is proposed.The method integrates Lasso regression and a random forest model to improve robustness under low-prob-ability conditions.An influencing factor set for transportation incident precursors is constructed by considering transport scale,operational efficiency,resource input,and operational intensity.Lasso regression combined with time-series cross-validation is applied for feature selection to alleviate multicollinearity under small-sample condi-tions.This procedure improves the stability and rationality of selected features.A random forest model is employed to predict transportation incident precursors.Feature importance analysis is applied to improve prediction accuracy.An error-driven model simplification strategy is used to reduce model complexity and enhance practical applicabili-ty.Civil aviation operational data of China from 2003 to 2022 are used for validation.Results indicate that the Las-so-random forest model achieves the lowest SRMSE value of 45.2 and the highest R2 value of 0.834.The model sig-nificantly outperforms linear regression and support vector regression models.After simplification,the SRMSE is further reduced by 6.14%.Based on the simplified model,flight hours and incident precursor occurrences for 2023 are predicted.The resulting en-route aircraft collision safety target level is,which satisfies applicable safety stan-dards.The proposed method provides a robust and operational framework for low-probability aviation risk assess-ment and safety target level formulation.