###### Abstract
Thus, there is a chance to advance the state of the art in turbulence identification by using in-flight data [13]. Modern machine learning algorithms have become highly efficient at identifying patterns in flight data. Routine flights usually gather a lot of data that can be utilized for many different purposes [13]. Since 2002, the aviation meteorological data relay (AMDAR) has included turbulence monitoring data, including EDR, according to the World Meteorological Organisation (WMO) [15]. Currently, the majority of domestic and international efforts are focused on EDR estimation. For example, in [1], [PERSON] et al. attempted to identify EDR using QAR data recorded from flights. In [2], [PERSON] et al. propose QAR data can be used as an additional source for measuring EDR. However, although the current EDR estimation algorithms exhibit overall consistency, potential estimation biases may arise due to disparities in the sources of flight datasets [16]. Therefore, we speculate whether it is feasible to bypass the EDR estimation and leverage machine learning algorithms to learn the patterns inducing turbulence from QAR data. In recent years, this conjecture has been validated by several machine learning algorithms, applied to the detection and prediction of turbulence in flight data [13; 17; 18]. In [17], [PERSON] et al. proposed utilizing QAR data to establish a random forest model for detecting flight turbulence, which demonstrated higher accuracy in a balanced sample state. However, the model tended to exhibit lower recognition accuracy when faced with imbalanced positive and negative samples. In addition to the instability of the model's performance, the authors did not conduct feature selection on the QAR data. This is a highly serious issue, as the success of data-driven modeling is highly dependent on the feature selection during the data process [8]. In [18], [PERSON] et al. proposed a machine learning approach utilizing Support Vector Classification (SVC) for predicting flight turbulence. The authors employed feature selection methods to reduce the dimensionality of the original data, enhancing the efficiency of data analysis. However, SVC's computational accuracy remained to be improved, as it did not address a method for optimizing SVC kernel parameters. In response to this issue, many researchers have opted for swarm-intelligence-based methods to optimize the parameters of machine learning models [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. This paper uses a QAR data-driven optimization approach based on data mining by utilizing machine learning algorithms to train a turbulence identification model around EDR. In this article, using the QAR data prior to training, we apply an RFE method to mine valuable features for turbulence identification in QAR data and utilize these features for machine learning. Subsequently, in order to establish a robust turbulence identification model, we opt for the WOA with rapid convergence to optimize parameters of the highly stable CatBoost model with a QAR data-driven approach. In the experimental part, QAR data with valuable subset of features are used to train the model, and then the trained model is used to process QAR data from other flights to verify its general applicability.