## 1 Introduction
Without a comprehensive analysis of the dust distribution patterns, achieving accurate predictions becomes challenging. Thus, accurately identifying the factors that affect dust concentration is crucial in the realm of predicting dust levels in opencast mining. In their study on the influencing factors of dust concentration, [PERSON] et al. analyzed the factors affecting dust concentration in the Haerwususu opencast coal mine. They determined the order of influencing factors as follows: coal production \(>\) boundary layer height \(>\) wind speed \(>\) temperature difference \(>\) temperature \(>\) humidity [21]. [PERSON] conducted an analysis of dust concentration characteristics and the influence of meteorological factors such as temperature, humidity, wind speed, and air pressure on dust levels, using data on dust concentration and weather conditions. The research also explained the reasons behind the variations in dust concentration. Findings revealed that dust levels at the open-pit mine peaked in March, November, and during winter, while they were lower in summer and autumn. The daily changes in humidity and temperature across different seasons displayed a \"herringbone\" and \"inverted herringbone\" pattern, respectively. Additionally, a dust concentration prediction model utilizing a long short-term memory (LSTM) neural network was created, achieving a fitting degree of approximately 0.88 in its predictions [22]. Additionally, in the interdisciplinary domain, many researchers utilize the random forest method for classification and regression tasks. One significant benefit of random forest is its ability to perform feature selection, allowing for the identification of the most important features from a larger set. This approach helps to remove less relevant factors, thereby enhancing the model's predictive accuracy [23]. [PERSON] et al. proposed a hybrid particle swarm optimization algorithm (PSO) to optimize the estimated PM concentration of the random forest (RF) model. The input variables included five indicators: wind direction, wind speed, temperature, humidity, and noise [24]. In conclusion, while research on dust prediction in opencast mines has yielded significant results and ongoing innovations in theoretical methods, the technology remains imperfect. When it comes to predicting dust concentration with multiple data features, there is still a reliance on a singular and fixed prediction approach. Statistical tools and machine learning algorithms both have their respective strengths and weaknesses (see Table 1). Additionally, the factors affecting dust concentration have not been thoroughly explored, and there is a subjective selection of prediction indicators [25]. Additionally, there is a limited amount of research on the time series data regarding dust concentration in open-pit mines, which makes it more challenging to create accurate predictions for dust levels in these environments. To address the aforementioned issues, this paper proposes the design of a real-time monitoring system for meteorological conditions and dust concentration in open-pit mines, based on the existing literature research. The system enables online monitoring of temperature, humidity, wind speed, wind direction, rainfall, noise, TSP, PM10, and PM2.5 in open-pit mines. A screening method for predicting dust concentration index sets based on random forest (RF) is proposed, and quantitatively screens the optimal index set. The open-pit mine's dust concentration prediction index system is developed to address the issue of subjective selection of dust concentration prediction indices. The genetic algorithm (GA) is utilized to optimize the least squares support vector machine (LSSVM), and the combined GA-LSSVM prediction model for dust concentration in open-pit mines is established. This approach overcomes the issues of premature, local convergence, and slow optimization speed associated with single algorithms, resulting in a more accurate prediction effect.