_Applied_  
_sciences_  
_Article_  
**Estimating PM\({}_{10}\) Concentration from Drilling**  
**Operations in Open-Pit Mines Using an Assembly of SVR and PSO**  
**[PERSON] 1,2,*\(\Box\)**, [PERSON] 3, [PERSON] 4,*\(\Box\)**, [PERSON] 5,6\(\Box\)**,  
**[PERSON] 7, [PERSON] 1,2, [PERSON] 3, [PERSON] 8 and [PERSON] 9,10,* 1**  
* 1 Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
* 2 Center for Mining, Electro-Mechanical Research, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
* 3 Department of Energy and Mineral Resources, College of Engineering, Dong-A University, Busan 49315, Korea
* 4 Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
* 5 Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang Ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
* 6 Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang Ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
* 7 Department of Mine Surveying, Hanoi University of Mining and Geology, Duc Thang, Bac Tu Liem, Hanoi 100000, Vietnam
* 8 Faculty of Environmental and Occupational Health, Hanoi University of Public Health, 1A Duc Thang Road, Duc Thang Ward, North Tu Liem District, Hanoi 100000, Vietnam
* 9 Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
* 10 Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
* * Correspondence: [EMAIL_ADDRESS] (X.-N.B. ); [EMAIL_ADDRESS] (H.N. ); [EMAIL_ADDRESS] (H.M.). Received: 7 June 2019; Accepted: 5 July 2019; Published: 12 July 2019  
**Featured Application: This study provided a new artificial intelligence system (i.e., PSO-SVR) to predict and control PM\({}_{10}\) concentration induced by drilling operations in open-pit mines. By the use of this system, the air quality can be managed as a part of the whole air quality in open-pit mines. Also, occupational diseases can be controlled and minimized by this system. **  
**Abstract:** Dust is one of the components causing heavy environmental pollution in open-pit mines, especially PM\({}_{10}\). Some pathologies related to the lung, respiratory system, and occupational diseases have been identified due to the effects of PM\({}_{10}\) in open-pit mines. Therefore, the prediction and control of PM\({}_{10}\) concentration in the production process are necessary for environmental and health protection. In this study, PM\({}_{10}\) concentration from drilling operations in the Coc Sau open-pit coal mine (Vietnam) was investigated and considered through a database including 245 datasets collected. A novel hybrid artificial intelligence model was developed based on support vector regression (SVR) and a swarm optimization algorithm (i.e., particle swarm optimization (PSO)), namely PSO-SVR, for estimating PM\({}_{10}\) concentration from drilling operations at the mine. Polynomial (P), radial basis function (RBF), and linear (L) kernel functions were considered and applied to the development of the PSO-SVR models in the present study, abbreviated as PSO-SVR-P, PSO-SVR-RBF, and PSO-SVR-L. Also, three benchmark artificial intelligence techniques, such as \(k\)-nearest neighbors (KNN), random forest (RF), and classification and regression trees (CART), were applied and developed for estimating PM\({}_{10}\) concentration and then compared with the PSO-SVR models. Root-mean-squared error (RMSE)and determination coefficient (R\({}^{2}\)) were used as the statistical criteria for evaluating the performance of the developed models. The results exhibited that the PSO algorithm had an essential role in the optimization of the hyper-parameters of the SVR models.