###### Abstract  
Fully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts' experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based on Extreme Gradient Boosting Regression Trees (XGBoost), which is developed based on the mapping relationships among geological parameters, fully mechanized mining face conditions, and the parameters of fully mechanized mining equipment. Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. Different optimization algorithms are used to optimize the hyperparameters of XGBoost, and the results show that the Whale Optimization Algorithm (WOA) outperforms other algorithms in terms of convergence speed and optimization effectiveness. By comparing different prediction algorithms, it is found that the WOA-XGBoost model achieves higher prediction accuracy on the test set, with an average absolute error of 0.0458, root mean square error of 0.1610, and a coefficient of determination (R\({}^{2}\)) of 0.9451. Finally, a RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment is established, which is suitable for lightly inclined mining faces. This model reduces input complexity, improves the selection speed, minimizes reliance on experts, and ensures prediction accuracy, providing an effective reference for the parameter selection of fully mechanized mining equipment. fullly mechanized mining face; three machines selection; machine learning; feature selection; parameter prediction model +
Footnote †: journal: Applied sciences  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-0000000-0000000000000000000000  
collaboration, forming a highly integrated and coordinated production system. Therefore, reasonably selecting their parameters is crucial for ensuring the efficient operation of the system [3]. However, traditional three-machine selection methods mainly determine equipment parameters by calculating theoretical parameters and combining manual experience with analogies from surrounding mine examples [4]. These methods can no longer meet the modern coal mining industry's requirements for high efficiency, low consumption, and safety. This approach has issues such as long selection cycles, low efficiency, and susceptibility to human biases. At the same time, traditional methods have certain limitations in matching equipment with the mining face, which can lead to unnecessary safety hazards and resource waste [5]. When equipment parameters are too large, production capacity and energy consumption may exceed the actual needs, causing an increase in the cost of mining per ton of coal by 15-30%. Conversely, when parameters are too small, it may result in insufficient support strength, increasing the risk of roof collapse. For example, the roof deformation rate may exceed 5% when the support center distance is too large, and in severe cases, this can cause equipment damage and casualties [6]. With the breakthrough of 5G, big data, Internet of Things, and other new-generation information technologies, the concept of intelligent mining has gradually been introduced and applied in coal mining to address complex decision-making problems [7]. Although these technologies have been widely applied in areas such as coal mine environmental prediction [8; 9; 10] and fully mechanized mining equipment coordination [11; 12; 13; 14], they still face certain challenges in the intelligent application of three-machine selection. For example, [PERSON] et al. [15] constructed a database of commonly used mining equipment and their performance parameters. Equipment selection is performed by inputting mining face condition parameters for performance parameter calculation, combining the \"reserve rule\" to output parameters and models.