## 1 Introduction  
Open-pit mining is one of the technologies that aim to pick up natural resources underground. However, the impact from the operations of open-pit mines, such as drilling, blasting, loading, transporting, and dumping are significant [1]. Also, a large area of land is occupied for work and dump sites. Other impacts on the property, water, and atmosphere due to open-pit mining activities are also considered to be significant [2; 3; 4]. Of those, dust concentration has a significant impact on public health and environmental pollution. The particles in the dust with an equivalent aerodynamic diameter smaller than 10 \(\upmu\)m (PM\({}_{10}\)) are considered as a hazardous factor for atmospheric and public health [5; 6; 7]. This dust is cause-related to lung, respiratory, and ocular diseases [3; 8]. The fauna and flora of the surrounding area are also affected by the dust caused by open-pit mining operations [9; 10]. In open-pit mines, many activities raise PM\({}_{10}\) dust, such as transporting, drilling-blasting, loading/unloading, and dumping [11; 12; 13; 14; 15; 16]. These activities can be divided into point, line, and area sources [17; 18]. Of those activities, drilling is, at 25% of the whole operation, one of the point sources that generates dust in the environment and is one of particular concern. Predicting and controlling PM\({}_{10}\) concentration in each operation is the basis of the development of the total predictive model for primary activities in open-pit mines (i.e., drilling, blasting, loading/unloading, transporting). Accurate prediction and strict control of PM\({}_{10}\) concentration from drilling operations in open-pit mines are essential to ensuring health and atmospheric safety, as well as providing the significant basis for the total predictive model. According to the United States Environmental Protection Agency (USEPA), dust concentration and emission can be predicted by various empirical equations. Several scholars have also proposed experimental equations to estimate dust concentration [19; 20; 21; 22; 23; 24]. However, those equations were not suitable for mining conditions in Vietnam, especially deep mines [25; 26]. In open-pit mines with deep mining, the problem of natural ventilation is often complicated due to the impact of mining depth because dust and toxic gases are often not circulated, making it challenging to calculate dust concentration as well as poisonous gases. [PERSON] [27] proposed several equations for predicting dust concentration in an open-pit coal mine of India; however, dust concentration from drilling operations was not considered in his study. Meanwhile, drilling operations are one of the main factors causing environmental pollution in open-pit mines, especially PM\({}_{10}\) emission. In recent years, quantitative models have been proven as effective methods to predict environmental issues and control atmospheric pollution. Artificial intelligence (AI) and its applications were considered as the robust tools for predicting and controlling environmental issues, especially in mining operations [28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. For estimating PM\({}_{10}\) concentration, Chelani and Gajghaneural network (ANN) based on the back-propagation (BP) algorithm to predict PM\({}_{10}\) concentration. The feasibility of ANN was interpreted in their study with promising results. By a similar approach, [PERSON] [46] also developed an ANN model to predict PM\({}_{10}\) and PM\({}_{2.5}\) concentration. In another research, [PERSON] and [PERSON] [47] successfully developed an ANN model for predicting PM\({}_{10}\) concentration in an open-cast coal mine of India with promising results. [PERSON] and [PERSON] [48] also considered and developed two ANN models based on the Autoregressive with external (ANNARX) method for predicting PM\({}_{10}\) and total suspended particles (TSP). Their study showed that ANN was a superior technique for predicting PM\({}_{10}\) and TSP based on several statistical criteria.