## 1 Introduction
[PERSON], [PERSON] [49] also developed a hybrid model using ANN and fuzzy logic, i.e., neuro-fuzzy for predicting TSP. As a result, they found that the neuro-fuzzy model provided a performance better than the ANN model. [PERSON], [PERSON] [50] also conducted a similar study using the ANN model for predicting PM\({}_{10}\) produced by drilling operations in an open-pit mine. [PERSON], [PERSON] [51] also developed an ANN model to predict various particulate matter (i.e., PM\({}_{0.23-0.3}\), PM\({}_{0.3-0.4}\), PM\({}_{0.4-0.5}\), PM\({}_{0.5-0.65}\), PM\({}_{0.65-0.8}\), PM\({}_{0.8-1}\), and PM\({}_{1-1.6}\)) with an excellent result. Table 1 summarizes several AI techniques in predicting PM\({}_{10}\) concentration and TSP. Also, similar and relevant work on the prediction of PM\({}_{10}\) concentration can be found at the following references [52; 53; 54; 55; 56; 57; 58; 59; 60; 61]. Given the previous work, it can be seen that it is feasible to use AI techniques to predict dust concentration as well as PM\({}_{10}\) emissions. It has been demonstrated that AI techniques, especially ANN, are significant in predicting and controlling dust concentration, and can form the basis of development of other models and dust emission monitoring methods. Our literature review showed that, although the studies of PM\({}_{10}\) concentration prediction in open-pit mines had been conducted, they are, however, still very sketchy. Furthermore, the intensity of PM\({}_{10}\) concentration in each area/mine/region/country is different due to the characteristics of rock mass as well as the meteorological conditions. Therefore, each area/mine/region needs to be specifically researched in order to have solutions aimed to ensure safety for the health of residents and employees and the surrounding environment. In this study, PM\({}_{10}\) concentration from drilling operations in open-pit mines was investigated and considered. The Coc Sau open-cast coal mine in Vietnam was selected as a case study for this aim. To this regard, many AI-related techniques, such as deep learning (DL), reinforcement learning (RL), recurrent neural networks (RNN), convolutional neural networks (CNN), generative adversarial networks (GAN), artificial neural network (ANN), and the adaptive-neuro fuzzy inference system (ANFIS), to name a few, can be applied; however, it is challenging to select which technique is the best for analyzing the concentration of PM\({}_{10}\) in open-pit mines. In practical engineering, simple machine learning and the optimization algorithms \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Reference** & **Technique** & **Objective** & **Result** \\ \hline
1 & [PERSON], [PERSON] [45] & ANN & PM\({}_{10}\) & RMSE = 7.9; R\({}^{2}\) = 0.89 \\ \hline
2 & [PERSON] [46] & ANN & PM\({}_{10}\) & RMSD = 2.21; r = 0.75 \\ \hline
3 & [PERSON] and [PERSON] [47] & ANN & PM\({}_{10}\) & RMSE = 0.0339; d = 0.9969 \\ \hline  & & & & \(\text{MSE}_{\text{PM10}}\) = 219.785; \\
4 & [PERSON], [PERSON] [48] & ANN & PM\({}_{10}\), TSP & \(\text{MSE}_{\text{TSP}}\) = 101.07; \\  & & & & \(\text{MSE}_{\text{TSP}}\) = 0.234 \\ \hline
5 & [PERSON], [PERSON] [49] & Neuro-fuzzy & PM\({}_{2.5}\) & R = 0.72 \\ \hline
6 & [PERSON], [PERSON] [50] & ANN & PM\({}_{10}\) & MSE = 0.00606; R\({}^{2}\) = 0.96 \\ \hline  & & & PM\({}_{0.23-0.3}\) & \\  & & & PM\({}_{0.3-0.4}\) & \\
7 & [PERSON], [PERSON] [51] & ANN & PM\({}_{0.4-0.5}\) & R = 0.806, 0.852, 0.808, 0.896, 0.698, \\  & & & PM\({}_{0.5-0.65}\) & 0.674, 0.788, respectively \\  & & & PM\({}_{0.65-0.8}\) & \\  & & & PM\({}_{0.8-1}\) & \\  & & & PM\({}_{1-1.6}\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of works using AI techniques for predicting PM\({}_{10}\) concentration and TSP. are often selected as the goal of soft computing models for simple regression problems due to their applicability and effectiveness.