## 2 The Basic Principle of RF-WOA-XGBoost
### RF-WOA-XGBoost Model Construction  
RF is used as the feature engineering method for input feature selection, XGBoost is employed as the prediction method to map input features to output features, and WOA is applied for hyperparameter optimization of the XGBoost algorithm. A prediction model for the parameters of the three machines in fully mechanized mining faces is constructed, as shown in the flowchart in Figure 1. The model construction process is as follows:  
1. Data cleaning and pre-processing: The three machines matching instance data are cleaned, and outliers are visualized using box plots. By analyzing the outliers in the box plot, data that do not meet the requirements are deleted, ensuring the integrity and rationality of the data. The data are uniformly processed, with non-numeric features encoded in a sequential manner to eliminate scale differences between features, improving the model's training efficiency and prediction accuracy.
2. Feature selection: The Pearson correlation coefficient is used to generate a heatmap for visual analysis of the linear correlation between input features. Features with high correlations (correlation coefficient greater than 0.8) are removed [36] to avoid the impact of multicollinearity on the model. Additionally, the features are normalized by scaling each feature value to the range \([0,1]\), ensuring that all features have consistent scales during model training, and reducing the impact of biases between features. The data are split into training and testing sets in an 8:2 ratio. The RF model is used to explore the nonlinear relationships between input and output features and to evaluate the contribution of each input feature to the target variable. The contributions of all features are ranked in descending order, and the most important features are selected. The XGBoost model is then trained by gradually adding input features, and the feasibility of the feature selection is validated through the peak stability of the R\({}^{2}\) mean.
3. Hyperparameter Optimization: The population size and the number of iterations for the WOA are set, and the hyperparameter range for the XGBoost model is determined. Each whale represents a combination of XGBoost hyperparameters, with the initial solution randomly generated. The key control parameters \(a\), \(A\), \(C\), and \(l\) in the WOA are updated to guide the search behavior of the whales. When \(p\geq 0.5\), spiral predation is performed; otherwise, when \(|A|\geq~{}1\), the whales perform encircling contraction, and when \(|A|<~{}1\), they conduct independent random search [37]. The fitness is recalculated based on the new position of the whales, and the current global best solution is saved until the iteration ends, resulting in the optimal hyperparameter combination.
4. Model Training and Evaluation: The performance of the XGBoost model with the optimal hyperparameters is evaluated on the training set. If \(R2~{}\geq~{}0.9\)[38], it indicates that the prediction performance is satisfactory and the model can be used. Otherwise, the data partitioning step is repeated, and retraining is performed until the required performance is achieved, resulting in the three machines parameter prediction model.  
Figure 1: Flowchart of the RF-WOA-XGBoost-based prediction process for the three machines parameters of fully mechanized mining face.