## 1 Introduction
Moreover, it is common to encounter local minimization issues with individual algorithms. Relying solely on primary algorithms like decision trees, support vector machines, and neural networks often makes it challenging to attain optimal prediction results. However, the likelihood of getting stuck in a local minimum can be significantly decreased when using a combined approach through iterative processes. Due to the extensive use of biological intelligent optimization algorithms, the leading prediction techniques today are integrated models that combine machine learning with optimization through biological intelligent algorithms [9]. With the widespread application of biological intelligent optimization algorithms, an increasing number of researchers are using the particle swarm optimization algorithm to optimize the prediction parameters of the model. Furthermore, the genetic algorithm, with its global optimization capability and high parallelism, is more popular than the particle swarm optimization algorithm. It is not only difficult to fall into a local optimum, but it also offers high accuracy and computational efficiency [10]. For example, [PERSON] et al. developed a genetic algorithm to optimize the BP neural network model for predicting the dust concentration in the time series of the mine working face. It has been verified that the high-precision prediction requirements were achieved [11]. [PERSON] et al. proposed a BP neural network model based on genetic optimization to predict the dust concentration in a coal mining face. The accuracy of the model has significantly improved compared with the traditional model [12]. The support vector machine (SVM) has been widely utilized due to its strong generalization ability, rapid learning speed, and effective noise resistance. In order to improve the classification and regression learning capabilities of support vector machines and enhance prediction accuracy, scholars have proposed an enhanced prediction model called the least squares support vector machine (LSSVM) algorithm. [PERSON] et al. developed a hybrid particle swarm optimization and genetic algorithm (HGAPSO) to optimize the least squares support vector machine (LSSVM) model for predicting the cetane number of biodiesel. The optimization performance of three combined models, HGAPSO-LSSVM, PSO-LSSVM, and GA-LSSVM, was compared [13]. [PERSON] et al. proposed a hybrid intelligent prediction model that combines the random forest (RF) and least squares support vector machine (LSSVM) algorithms. The RF algorithm is used to establish the optimal index set for predicting the chloride ion penetration resistance of high-performance concrete. Additionally, the LSSVM algorithm accurately predicts the chloride ion penetration resistance of high-performance concrete (HPC) [14]. [PERSON] et al. developed a model for predicting dye concentration based on PSO-LSSVM. The results show that the efficiency (Ep) of the model is above 96%, indicating high prediction accuracy and strong dye specificity [15]. [PERSON] et al. applied a method based on the least squares support vector machine (LSSVM) and the genetic algorithm (GA) to predict the coefficient of performance (COP) and regulate the load of each unit in the chiller system. The results indicate that the LSSVM model, optimized by the genetic algorithm, demonstrates sufficient accuracy for predicting COP [16]. [PERSON] et al. utilized Fourier series to simulate the shape of a blasting muck pile and established a prediction model for its shape and effect based on GA-LSSVM, achieving high prediction accuracy [17]. While LSSVM is not commonly used in mine dust prediction, it has been successfully applied in predicting telecom flow [18], storage for wind energy [19], blasting effects [20], and other fields. The environment of opencast mines is intricate and constantly changing, leading to significant fluctuations in dust concentration due to various influencing factors.