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Hoang Nguyen
Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Wards, Bac Tu Liem Dist., Hanoi, 100000, Vietnam

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Journal article
Published: 18 August 2021 in Resources Policy
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This paper aims at exploring the relationship between production factors, ore grades, and life of mine for forecasting mining capital cost (MCC) for open pit mining projects. Accordingly, the relationship between annual mine and mill production (MineAP, MillAP), stripping ratio (SR), reserve mean grade (RMG), the life of mine (LOM), and MCC of 80 open pit mining projects were investigated and thoroughly evaluated. The dataset was then divided into two sections, with 56 observations used to develop the forecast models. The remaining 24 observations were used to test the accuracy of the developed models. Subsequently, the cascade feedforward neural network (CFNN) was developed to forecast MCC based on the influential parameters. In order to improve the accuracy of the CFNN model, the salp swarm optimization (SalpSO) algorithm was applied to train the CFNN model and optimize the weights of the model, called the SalpSO-CFNN model. The benchmark models which were developed in the previous studies, such as support vector machine (SVM), classification and regression tree (CART), and multiple layers perceptron (MLP) neural network, were also developed in this study to compare with the proposed SalpSO-CFNN model in terms of MCC forecast. The results revealed that production factors, ore grades, and LOM are closely related to MCC, and they are statistically significant. The forecast results also indicated that the proposed novel SalpSO-CFNN model provided a good accuracy with a mean absolute error (MAE) of 179.567, root-mean-squared error (RMSE) of 248.401, and determination coefficient (R2) of 0.980. This result is higher by 18% compared with the CART model and 2–6% compared with the remaining forecast models. A sensitivity analysis also indicated that MineAP, MillAP are the most influential parameters on the forecast of MCC, and they should be specially taken into account when forecasting MCC of open pit mining projects.

ACS Style

Xiaolei Zheng; Hoang Nguyen; Xuan-Nam Bui. Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model. Resources Policy 2021, 74, 102300 .

AMA Style

Xiaolei Zheng, Hoang Nguyen, Xuan-Nam Bui. Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model. Resources Policy. 2021; 74 ():102300.

Chicago/Turabian Style

Xiaolei Zheng; Hoang Nguyen; Xuan-Nam Bui. 2021. "Exploring the relation between production factors, ore grades, and life of mine for forecasting mining capital cost through a novel cascade forward neural network-based salp swarm optimization model." Resources Policy 74, no. : 102300.

Journal article
Published: 20 July 2021 in Geocarto International
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ACS Style

Romulus Costache; Alireza Arabameri; Hossein Moayedi; Quoc Bao Pham; M. Santosh; Hoang Nguyen; Manish Pandey; Binh Thai Pham. Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Geocarto International 2021, 1 -28.

AMA Style

Romulus Costache, Alireza Arabameri, Hossein Moayedi, Quoc Bao Pham, M. Santosh, Hoang Nguyen, Manish Pandey, Binh Thai Pham. Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree. Geocarto International. 2021; ():1-28.

Chicago/Turabian Style

Romulus Costache; Alireza Arabameri; Hossein Moayedi; Quoc Bao Pham; M. Santosh; Hoang Nguyen; Manish Pandey; Binh Thai Pham. 2021. "Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, naïve Bayes, XGBoost and classification and regression tree." Geocarto International , no. : 1-28.

Original paper
Published: 12 July 2021 in Natural Resources Research
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Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of structures, and changing the flow direction of groundwater. Therefore, many studies in recent years have focused on the accurate prediction and control of GV in open-pit mines. In this study, three intelligent hybrid models were examined for predicting GV based on different nature-inspired optimization algorithms and deep neural networks. Accordingly, a deep neural network (DNN) was developed for predicting GV under the enhancement of deep learning techniques. Subsequently, aiming at improving the accuracy and reducing the error of the DNN model in terms of the prediction of blast-induced GVs, three optimization algorithms based on the behaviors of whale, Harris hawks, and particle swarm in nature (abbreviated as WOA, HHOA, and PSOA, respectively) were considered and applied, namely HHOA–DNN, WOA–DNN, and PSOA–DNN, respectively. The results were then compared with those of the conventional DNN model through various performance indices; 229 blasting events in an open-pit coal mine in Vietnam were processed for this aim. Finally, it was found that the proposed intelligent hybrid models outperform the DNN model with deep learning techniques, although it is a state-of-the-art model that has been recommended and claimed by previous researchers. In particular, HHOA, WOA, and PSOA (with global optimization) further improved the accuracy of the DNN model by 1–2%. Of those, the HHOA–DNN model provided the highest performance with a mean-squared-error of 2.361, root mean squared error of 1.537, mean absolute percentage error of 0.123, variance accounted for of 93.015, and coefficient determination of 0.930 on the testing dataset. The findings also revealed that the explosive charge per blast, monitoring distance, and time delay per each blasting group are necessary parameters for predicting GV.

ACS Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Dinh-An Nguyen; Le Thi Thu Hoa; Qui-Thao Le; Le Thi Huong Giang. Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network. Natural Resources Research 2021, 1 -23.

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran, Dinh-An Nguyen, Le Thi Thu Hoa, Qui-Thao Le, Le Thi Huong Giang. Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network. Natural Resources Research. 2021; ():1-23.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Dinh-An Nguyen; Le Thi Thu Hoa; Qui-Thao Le; Le Thi Huong Giang. 2021. "Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network." Natural Resources Research , no. : 1-23.

Journal article
Published: 04 July 2021 in Engineering with Computers
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ACS Style

Bui Hoang Bac; Hoang Nguyen; Nguyen Thi Thanh Thao; Vo Thi Hanh; Le Thi Duyen; Nguyen Tien Dung; Nguyen Khac Du; Nguyen Huu Hiep. Estimating heavy metals absorption efficiency in an aqueous solution using nanotube-type halloysite from weathered pegmatites and a novel Harris hawks optimization-based multiple layers perceptron neural network. Engineering with Computers 2021, 1 .

AMA Style

Bui Hoang Bac, Hoang Nguyen, Nguyen Thi Thanh Thao, Vo Thi Hanh, Le Thi Duyen, Nguyen Tien Dung, Nguyen Khac Du, Nguyen Huu Hiep. Estimating heavy metals absorption efficiency in an aqueous solution using nanotube-type halloysite from weathered pegmatites and a novel Harris hawks optimization-based multiple layers perceptron neural network. Engineering with Computers. 2021; ():1.

Chicago/Turabian Style

Bui Hoang Bac; Hoang Nguyen; Nguyen Thi Thanh Thao; Vo Thi Hanh; Le Thi Duyen; Nguyen Tien Dung; Nguyen Khac Du; Nguyen Huu Hiep. 2021. "Estimating heavy metals absorption efficiency in an aqueous solution using nanotube-type halloysite from weathered pegmatites and a novel Harris hawks optimization-based multiple layers perceptron neural network." Engineering with Computers , no. : 1.

Original paper
Published: 01 July 2021 in Natural Resources Research
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Innovation efforts in developing soft computing models (SCMs) of researchers and scholars are significant in recent years, especially for problems in the mining industry. So far, many SCMs have been proposed and applied to practical engineering to predict ground vibration intensity (BIGV) induced by mine blasting with high accuracy and reliability. These models significantly contributed to mitigate the adverse effects of blasting operations in mines. Despite the fact that many SCMs have been introduced with promising results, but ambitious goals of researchers are still novel SCMs with the accuracy improved. They aim to prevent the damages caused by blasting operations to the surrounding environment. This study, therefore, proposed a novel SCM based on a robust meta-heuristic algorithm, namely Hunger Games Search (HGS) and artificial neural network (ANN), abbreviated as HGS–ANN model, for predicting BIGV. Three benchmark models based on three other meta-heuristic algorithms (i.e., particle swarm optimization (PSO), firefly algorithm (FFA), and grasshopper optimization algorithm (GOA)) and ANN, named as PSO–ANN, FFA–ANN, and GOA–ANN, were also examined to have a comprehensive evaluation of the HGS–ANN model. A set of data with 252 blasting operations was collected to evaluate the effects of BIGV through the mentioned models. The data were then preprocessed and normalized before splitting into individual parts for training and validating the models. In the training phase, the HGS algorithm with the optimal parameters was fine-tuned to train the ANN model to optimize the ANN model's weights. Based on the statistical criteria, the HGS–ANN model showed its best performance with an MAE of 1.153, RMSE of 1.761, R2 of 0.922, and MAPE of 0.156, followed by the GOA–ANN, FFA–ANN and PSO–ANN models with the lower performances (i.e., MAE = 1.186, 1.528, 1.505; RMSE = 1.772, 2.085, 2.153; R2 = 0.921, 0.899, 0.893; MAPE = 0.231, 0.215, 0.225, respectively). Based on the outstanding performance, the HGS–ANN model should be applied broadly and across a swath of open-pit mines to predict BIGV, aiming to optimize blast patterns and reduce the environmental effects.

ACS Style

Hoang Nguyen; Xuan-Nam Bui. A Novel Hunger Games Search Optimization-Based Artificial Neural Network for Predicting Ground Vibration Intensity Induced by Mine Blasting. Natural Resources Research 2021, 1 -16.

AMA Style

Hoang Nguyen, Xuan-Nam Bui. A Novel Hunger Games Search Optimization-Based Artificial Neural Network for Predicting Ground Vibration Intensity Induced by Mine Blasting. Natural Resources Research. 2021; ():1-16.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui. 2021. "A Novel Hunger Games Search Optimization-Based Artificial Neural Network for Predicting Ground Vibration Intensity Induced by Mine Blasting." Natural Resources Research , no. : 1-16.

Journal article
Published: 25 June 2021 in Resources Policy
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The focus of this study aims at developing two novel hybrid intelligence models for forecasting copper prices in the future with high accuracy based on the extreme learning machine (ELM) and two meta-heuristic algorithms (i.e., particle swarm optimization (PSO) and genetic algorithm (GA)), named as PSO-ELM and GA-ELM models. Accordingly, the time series datasets of the copper price for thirty years were collected based on the influencing parameters, such as crude oil, iron ore, gold, silver, and natural gas prices. Furthermore, the exchange rate of the four largest countries in copper-producing, including Chile (USD/CLP), China (USD/CNY), Peru (USD/PEN), and Australia (USD/AUD), were also considered to evaluate the copper prices. The GA and PSO algorithms then optimized the weights and biases of the ELM model to reduce the error of the ELM model for forecasting copper price. The traditional ELM model (without optimization), and artificial neural networks (ANN) were also developed as the comparative models for resulting in convincing experimental results in the proposed PSO-ELM and GA-ELM models. The results indicated that the proposed hybrid PSO-ELM and GA-ELM models could forecast copper price with higher accuracy and reliability over the traditional ELM and ANN models. Of those, the PSO-ELM yielded the most dominant accuracy with a root-mean-squared error (RMSE) of 304.943, mean absolute error (MAE) of 241.946, mean absolute percentage error (MAPE) of 0.037, and mean absolute scaled error (MASE) of 0.933. The t-test and Wilcoxon test also demonstrated the statistical significance of the proposed models and the best 95% confident interval of the PSO-ELM model with the range of $177.046 to $67.054 with p-value = 2.589e-05. Whereas, the GA-ELM model provided the forecasted copper price higher $137.233 than the actual copper price, and the 95% confidence interval is from $189.672 to $84.793 with p-value = 1.027e-06.

ACS Style

Hong Zhang; Hoang Nguyen; Xuan-Nam Bui; Biswajeet Pradhan; Ngoc-Luan Mai; Diep-Anh Vu. Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms. Resources Policy 2021, 73, 102195 .

AMA Style

Hong Zhang, Hoang Nguyen, Xuan-Nam Bui, Biswajeet Pradhan, Ngoc-Luan Mai, Diep-Anh Vu. Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms. Resources Policy. 2021; 73 ():102195.

Chicago/Turabian Style

Hong Zhang; Hoang Nguyen; Xuan-Nam Bui; Biswajeet Pradhan; Ngoc-Luan Mai; Diep-Anh Vu. 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms." Resources Policy 73, no. : 102195.

Journal article
Published: 24 June 2021 in Resources Policy
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Copper is one of the valuable natural resources, and it was widely used in many different industries. The complicated fluctuations of copper prices can significantly affect other industries. Therefore, this study aims to develop and propose several forecast models for forecasting monthly copper prices in the future based on various algorithms in machine learning, including multi-layer perceptron (MLP) neural network, k-nearest neighbors (KNN), support vector machine (SVM), gradient boosting tree (GBT), and random forest (RF). The monthly copper price dataset from January 1990 to December 2019 was collected for this aim based on other metals and natural gas prices. In addition, the influence of currency exchange rates of the countries that have the largest copper production around the world was also taken into account and used as input variables for forecasting copper price. The different set of predictors (t, t-1, t-2, t-3, t-4. t-5) were considered to forecast monthly copper prices based on the mentioned machine learning techniques. The results revealed that the currency exchange rates of the countries that have the most abundant copper production around the world have a significant effect on the volatility of monthly copper prices in the world, and they should be used to forecast monthly copper prices in the future. A comprehensive comparison of various machine learning techniques shows that MLP neural network (with deep learning techniques) is the best method for forecasting monthly copper price with an MAE of 228.617 and RMSE of 287.539. Whereas, the other models, such as SVM, RF, KNN, and GBT, provided higher errors with an MAE in the range of 308.691–453.147, RMSE in the range of 393.599–552.208. In this sense, MLP neural network can be used as a reliable tool to forecast copper prices in the future.

ACS Style

Hong Zhang; Hoang Nguyen; Diep-Anh Vu; Xuan-Nam Bui; Biswajeet Pradhan. Forecasting monthly copper price: A comparative study of various machine learning-based methods. Resources Policy 2021, 73, 102189 .

AMA Style

Hong Zhang, Hoang Nguyen, Diep-Anh Vu, Xuan-Nam Bui, Biswajeet Pradhan. Forecasting monthly copper price: A comparative study of various machine learning-based methods. Resources Policy. 2021; 73 ():102189.

Chicago/Turabian Style

Hong Zhang; Hoang Nguyen; Diep-Anh Vu; Xuan-Nam Bui; Biswajeet Pradhan. 2021. "Forecasting monthly copper price: A comparative study of various machine learning-based methods." Resources Policy 73, no. : 102189.

Original paper
Published: 22 June 2021 in Natural Resources Research
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In surface mining, blasting is an indispensable method for fragmenting rock masses. Nevertheless, it can inherently induce many side effects like ground vibrations. At high intensities, the ground vibrations generated because of blasting operations can destroy structures and buildings. Also, in areas with adverse geological conditions, such vibrations can cause bench and slope failures. Therefore, the accurate prediction of ground vibration intensity (GVI) has critical implications in mitigating and controlling the adverse effects along with sustainable development and responsible mining. In this research, a novel intelligent model was proposed to predict GVI based on the hybridization of autoencoder neural networks (AutoencoderNN) and support vector machine regression (SVR), and it was named AutoencoderNN-SVR. Nine input variables were utilized to estimate GVI: borehole diameter, bench height, borehole length, burden, spacing, hardness coefficient, powder factor, maximum explosive charged per delay, and monitoring distance. Two hundred ninety-seven blasting events were collected, analyzed, and evaluated to achieve this aim. Also, the traditional SVR model without the support of AutoencoderNN, an empirical equation (i.e., USBM), and a nonlinear model based on gene expression programing were applied in this research and compared with the proposed AutoencoderNN-SVR model in terms of GVI prediction. Then, the models' obtained results were analyzed and computed through statistical indices, such as root mean squared error (RMSE) and coefficient of determination (R2). The AutoencoderNN-SVR's ensemble model was found to have obtained the highest accuracy and lowest error (i.e., RMSE = 1.232 and R2 = 0.887) compared to the other models and is an insight in predicting GVI in mine blasting with high reliability. An autoencoder neural network was investigated to predict GVI in mine blasting; An autoencoder neural network was combined with support vector regression to generate a robust hybrid model (AutoencoderNN-SVR) to predict GVI in mine blasting; The proposed AutoencoderNN-SVR model was compared with the empirical, SVR, and GEP models; The proposed AutoencoderNN-SVR model was introduced as a novel and robust technique for predicting GVI with high accuracy.

ACS Style

Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Romulus Costache. Estimation of Ground Vibration Intensity Induced by Mine Blasting using a State-of-the-Art Hybrid Autoencoder Neural Network and Support Vector Regression Model. Natural Resources Research 2021, 1 -12.

AMA Style

Bo Ke, Hoang Nguyen, Xuan-Nam Bui, Romulus Costache. Estimation of Ground Vibration Intensity Induced by Mine Blasting using a State-of-the-Art Hybrid Autoencoder Neural Network and Support Vector Regression Model. Natural Resources Research. 2021; ():1-12.

Chicago/Turabian Style

Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Romulus Costache. 2021. "Estimation of Ground Vibration Intensity Induced by Mine Blasting using a State-of-the-Art Hybrid Autoencoder Neural Network and Support Vector Regression Model." Natural Resources Research , no. : 1-12.

Journal article
Published: 07 June 2021 in Ecological Indicators
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Forest fire disaster is currently the subject of intense research worldwide. The development of accurate strategies to prevent potential impacts and minimize the occurrence of disastrous events as much as possible requires modeling and forecasting severe conditions. In this study, we developed five new hybrid machine learning algorithms namely, Frequency Ratio-Multilayer Perceptron (FR-MLP), Frequency Ratio-Logistic Regression (FR-LR), Frequency Ratio-Classification and Regression Tree (FR-CART), Frequency Ratio-Support Vector Machine (FR-SVM), and Frequency Ratio-Random Forest (FR-RF), for mapping forest fire susceptibility in the north of Morocco. To this end, a total of 510 points of historic forest fires as the forest fire inventory map and 10 independent causal factors including elevation, slope, aspect, distance to roads, distance to residential areas, land use, normalized difference vegetation index (NDVI), rainfall, temperature, and wind speed were used. The area under the receiver operating characteristics (ROC) curves (AUC) was computed to assess the effectiveness of the models. The results of conducting proposed models indicated that RF-FR achieved the highest performance (AUC = 0.989), followed by SVM-FR (AUC = 0.959), MLP-FR (AUC = 0.858), CART-FR (AUC = 0.847), LR-FR (AUC = 0.809) in the forecasting of the forest fire. The outcome of this research as a prediction map of forest fire risk areas can provide crucial support for the management of Mediterranean forest ecosystems. Moreover, the results demonstrate that these novel developed hybrid models can increase the accuracy and performance of forest fire susceptibility studies and the approach can be applied to other areas.

ACS Style

Meriame Mohajane; Romulus Costache; Firoozeh Karimi; Quoc Bao Pham; Ali Essahlaoui; Hoang Nguyen; Giovanni Laneve; Fatiha Oudija. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators 2021, 129, 107869 .

AMA Style

Meriame Mohajane, Romulus Costache, Firoozeh Karimi, Quoc Bao Pham, Ali Essahlaoui, Hoang Nguyen, Giovanni Laneve, Fatiha Oudija. Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area. Ecological Indicators. 2021; 129 ():107869.

Chicago/Turabian Style

Meriame Mohajane; Romulus Costache; Firoozeh Karimi; Quoc Bao Pham; Ali Essahlaoui; Hoang Nguyen; Giovanni Laneve; Fatiha Oudija. 2021. "Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area." Ecological Indicators 129, no. : 107869.

Journal article
Published: 03 June 2021 in Chemosphere
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The efforts of this study aimed to evaluate the feasibility of the nanotubular halloysites in weathered pegmatites (NaHWP) for removing heavy metals (i.e., Cd2+, Pb2+) from water. Furthermore, two novel intelligent models, such as teaching-learning-based optimization (TLBO)-artificial neural network (ANN), and TLBO-support vector regression (SVR), named as TLBO-ANN and TLBO-SVR models, respectively, were proposed to predict the Cd2+ and Pb2+ absorption efficiencies from water using the NaHWP absorbent. Databases used, including 53 experiments for Pb2+ absorption and 56 experiments for Cd2+ absorption from water, under the catalysis of different conditions, such as initial concentration of Pb2+ and Cd2+, solution pH, adsorbent weight, and contact time. Subsequently, the TLBO-ANN and TLBO-SVR models were developed and applied to predict the efficiencies of Cd2+ and Pb2+ absorption from water, aiming to evaluate the role as well as the effects of different conditions on the absorption efficiencies using the NaHWP absorbent. The standalone ANN and SVM models were also taken into consideration and compared with the proposed hybrid models (i.e., TLBO-ANN and TLBO-SVR). The results showed that the NaHWP detected in a Kaolin mine (Vietnam) with 70% nanotubular halloysites is a potential adsorbent for water treatment to eliminate heavy metals from water. The two novel hybrid models proposed, i.e., TLBO-ANN and TLBO-SVR, also yielded the dominant performances and accuracies in predicting the Cd2+ and Pb2+ absorption efficiencies from water, i.e., RMSE = 1.190 and 1.102, R2 = 0.951 and 0.957, VAF = 94.436 and 95.028 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water; RMSE = 3.084 and 3.442, R2 = 0.971 and 0.965, VAF = 96.499 and 96.415 for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Furthermore, the validation results also demonstrated these findings in practice through 23 experiments with the accuracies of 98.3% and 98.37% for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Pb2+ absorption efficiency from water; the accuracies of 98.3% and 97.46% for the TLBO-ANN and TLBO-SVR models, respectively, in predicting the Cd2+ absorption efficiency from water. Besides, solution pH was evaluated as the most critical parameter that can be adjusted to enhance the performance of the absorption of the heavy metals in this study. By using the NaHWP absorbent and the novel proposed intelligent models developed, heavy metals can be eliminated entirely from water, providing pure water/clean freshwater without any risk of adverse health effects for the short term or long term.

ACS Style

Bui Hoang Bac; Hoang Nguyen; Nguyen Thi Thanh Thao; Le Thi Duyen; Vo Thi Hanh; Nguyen Tien Dung; Luong Quang Khang; Do Manh An. Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm. Chemosphere 2021, 282, 131012 .

AMA Style

Bui Hoang Bac, Hoang Nguyen, Nguyen Thi Thanh Thao, Le Thi Duyen, Vo Thi Hanh, Nguyen Tien Dung, Luong Quang Khang, Do Manh An. Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm. Chemosphere. 2021; 282 ():131012.

Chicago/Turabian Style

Bui Hoang Bac; Hoang Nguyen; Nguyen Thi Thanh Thao; Le Thi Duyen; Vo Thi Hanh; Nguyen Tien Dung; Luong Quang Khang; Do Manh An. 2021. "Performance evaluation of nanotubular halloysites from weathered pegmatites in removing heavy metals from water through novel artificial intelligence-based models and human-based optimization algorithm." Chemosphere 282, no. : 131012.

Journal article
Published: 27 May 2021 in Journal of Cleaner Production
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In recent years, the strong development of urban areas and rapid population growth have contributed significantly to environmental pollution issues, especially SW. Of those, municipal solid waste (MSW) is considered a major concern of waste treatment plants. Nowadays, with the development of science and technology, MSW has been treated and recycled to recover energy. However, the issue of energy recovery and optimization from MSW remains a challenge for waste treatment plants. Therefore, a novel artificial intelligence approach was proposed in this study for predicting the gas yield (GY) generated by energy recovery from MSW with high accuracy. Accordingly, a deep neural network (DNN) was developed to predict GY from MSW. Subsequently, the Moth-Flame optimization (MFO) algorithm was applied to optimize the DNN model and improve its accuracy, called MFO-DNN model. The findings revealed that both the DNN and MFO-DNN models predicted GY very well. Of those, the proposed MFO-DNN model provided dominant performance than the DNN model. Based on the proposed MFO-DNN model, the toxic gases can be thoroughly controlled and optimized to recover the gas field from MSW for waste treatment plants, minimizing negative impacts on the surrounding environment.

ACS Style

Libing Yang; Hoang Nguyen; Xuan-Nam Bui; Trung Nguyen-Thoi; Jian Zhou; Jianing Huang. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. Journal of Cleaner Production 2021, 311, 127672 .

AMA Style

Libing Yang, Hoang Nguyen, Xuan-Nam Bui, Trung Nguyen-Thoi, Jian Zhou, Jianing Huang. Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm. Journal of Cleaner Production. 2021; 311 ():127672.

Chicago/Turabian Style

Libing Yang; Hoang Nguyen; Xuan-Nam Bui; Trung Nguyen-Thoi; Jian Zhou; Jianing Huang. 2021. "Prediction of gas yield generated by energy recovery from municipal solid waste using deep neural network and moth-flame optimization algorithm." Journal of Cleaner Production 311, no. : 127672.

Journal article
Published: 23 May 2021 in Journal of Environmental Management
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Heavy metal adsorption onto biochar is an effective method for the treatment of the heavy metal contamination of water and wastewater. This study aims to evaluate the heavy metals sorption efficiency of different biochar characteristics and propose a novel intelligence method for predicting the sorption efficiency of heavy metal onto biochar with high accuracy based on the back-propagation neural network (BPNN) and fuzzy C-means clustering algorithm (FCM), named as FCM-BPNN. Accordingly, the FCM algorithm was used to simulate the properties of metal adsorption data and divide them into clusters with similar features. The clustering results showed that the FCM algorithm simulated metal adsorption data's properties very well and classified them based on biochar characteristics and adsorption conditions. Afterward, BPNN models were well-developed based on these clusters, and their outcomes were then combined (i.e., FCM-BPNN). The results indicated that the FCM-BPNN model could predict heavy metal's sorption efficiency onto biochar with a promising result (i.e., RMSE of 0.036, R2 of 0.987, RSE of 0.006, MAPE of 0.706, and VAF of 98.724). Whereas the BPNN model, without optimizing the FCM algorithm, was proved with lower performance (RMSE = 0.050, R2 = 0.977, RSE = 0.011, MAPE = 0.802, and VAF = 97.662). These findings revealed that the FCM algorithm's presence impressively improved the BPNN model's accomplishment in predicting heavy metal's sorption efficiency onto biochar, and the proposed FCM-BPNN model can improve water/wastewater treatment plants' quality and provide a more efficient process for heavy metals with performance superiority.

ACS Style

Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Trung Nguyen-Thoi. Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network. Journal of Environmental Management 2021, 293, 112808 .

AMA Style

Bo Ke, Hoang Nguyen, Xuan-Nam Bui, Hoang-Bac Bui, Trung Nguyen-Thoi. Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network. Journal of Environmental Management. 2021; 293 ():112808.

Chicago/Turabian Style

Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Trung Nguyen-Thoi. 2021. "Prediction of the sorption efficiency of heavy metal onto biochar using a robust combination of fuzzy C-means clustering and back-propagation neural network." Journal of Environmental Management 293, no. : 112808.

Correction
Published: 30 March 2021 in Acta Geophysica
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ACS Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Pham Van Hoa; Dinh-An Nguyen; Le Thi Thu Hoa; Qui-Thao Le; Ngoc-Hoan Do; Tran Dinh Bao; Hoang-Bac Bui; Hossein Moayedi. Correction to: A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophysica 2021, 1 -2.

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran, Pham Van Hoa, Dinh-An Nguyen, Le Thi Thu Hoa, Qui-Thao Le, Ngoc-Hoan Do, Tran Dinh Bao, Hoang-Bac Bui, Hossein Moayedi. Correction to: A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine. Acta Geophysica. 2021; ():1-2.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Pham Van Hoa; Dinh-An Nguyen; Le Thi Thu Hoa; Qui-Thao Le; Ngoc-Hoan Do; Tran Dinh Bao; Hoang-Bac Bui; Hossein Moayedi. 2021. "Correction to: A comparative study of empirical and ensemble machine learning algorithms in predicting air over-pressure in open-pit coal mine." Acta Geophysica , no. : 1-2.

Correction
Published: 23 March 2021 in Acta Geophysica
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Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Dao Trong Cuong. Correction to: Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophysica 2021, 69, 427 -428.

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Hoang-Bac Bui, Dao Trong Cuong. Correction to: Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study. Acta Geophysica. 2021; 69 (2):427-428.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Dao Trong Cuong. 2021. "Correction to: Developing an XGBoost model to predict blast-induced peak particle velocity in an open-pit mine: a case study." Acta Geophysica 69, no. 2: 427-428.

Journal article
Published: 09 March 2021 in Chemosphere
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Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal’s sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions. Therefore, this study implicates the feasibility of biochar systems in the heavy metal sorption in water/wastewater and the use of artificial intelligence (AI) models in investigating efficiency sorption of heavy metal on biochar. Accordingly, this work investigated and proposed 20 artificial intelligent models for forecasting the sorption efficiency of heavy metal onto biochar based on five machine learning algorithms and bagging technique (BA). Accordingly, support vector machine (SVM), random forest (RF), artificial neural network (ANN), M5Tree, and Gaussian process (GP) algorithms were used as the key algorithms for the aim of this study. Subsequently, the individual models were bagged with each other to generate new ensemble models. Finally, 20 intelligent models were developed and evaluated, including SVM, RF, M5Tree, GP, ANN, BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN. Of those, the hybrid models (i.e., BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN) are introduced as the novelty of this study for estimating the heavy metal’s sorption efficiency on the biochar systems. Also, the biochar characteristics, metal sources, and environmental conditions were comprehensively assessed and used, and they are considered as a novelty of the study as well. For this aim, a dataset of sorption efficiency of heavy metal was collected and processed with 353 experimental tests. Various performance indexes were applied to evaluate the models, such as RMSE, R2, MAE, color intensity, Taylor diagram, box and whiskers plots. This study’s findings revealed that AI models could predict heavy metal’s sorption efficiency onto biochar with high reliability, and the efficiency of the ensemble models is higher than those of individual models. The results also reported that the SVM-ANN ensemble model is the most superior model among 20 developed models. The predictive model proposed that heavy metal’s efficiency sorption on biochar can be accurately forecasted and early warning for the water pollution by heavy metal.

ACS Style

Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Yosoon Choi; Jian Zhou; Hossein Moayedi; Romulus Costache; Thao Nguyen-Trang. Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models. Chemosphere 2021, 276, 130204 .

AMA Style

Bo Ke, Hoang Nguyen, Xuan-Nam Bui, Hoang-Bac Bui, Yosoon Choi, Jian Zhou, Hossein Moayedi, Romulus Costache, Thao Nguyen-Trang. Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models. Chemosphere. 2021; 276 ():130204.

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Bo Ke; Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Yosoon Choi; Jian Zhou; Hossein Moayedi; Romulus Costache; Thao Nguyen-Trang. 2021. "Predicting the sorption efficiency of heavy metal based on the biochar characteristics, metal sources, and environmental conditions using various novel hybrid machine learning models." Chemosphere 276, no. : 130204.

Original article
Published: 27 February 2021 in Engineering with Computers
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The present paper's primary goal is to propose a novel hybrid model with high reliability to predict peak particle velocity (PPV)—a ground vibration evaluation unit in mine blasting. This model is based on the coupling of the multivariate adaptive regression splines (MARS), particle swarm optimization (PSO), and multi-layer perceptron neural networks (MLP). To this end, a strategy of stacking the MARS models was applied. Multiple MARS models were developed first with different hyper-parameters. Subsequently, the outcome predictions from these MARS models were merged as a new data set. The MLP model was then developed based on the newly generated data set, called the MARS–MLP model. To improve the accuracy and reduction of the MARS–MLP model's error, the PSO algorithm was applied in terms of optimization of the MARS–MLP's weights, called the MARS–PSO–MLP model. The proposed MARS–PSO–MLP model was then compared with the stand-alone MARS, MLP, empirical models, and the hybrid PSO–MLP model (without stacking MARS models) using the same data set. The results revealed that the proposed strategies could significantly boost the MARS and MLP models' performance with the PSO algorithm's effective help. The proposed MARS–PSO–MLP model yielded the highest accuracy and reliability with a root-mean-squared error (RMSE) of 1.569, mean absolute error (MAE) of 1.017, and squared-correlation (R2) of 0.902. In comparison, the stand-alone models (i.e., MARS and MLP) and the hybrid model of PSO–MLP provided lower performances with an RMSE of 1.582 to 1.704, MAE of 0.941 to 1.427, and R2 of 0.871 to 0.891. In contrast, poor performance with an RMSE of 5.059, MAE of 3.860, and R2 of 0.127 was found for the empirical model, and it is not a reliable method to predict PPV in this study. This work's findings also indicated that explosive charge per delay, monitoring distance, spacing, powder factor, and burden have significant effects on PPV, the incredibly explosive charge per delay, and monitoring distance. Remarkable, the stemming variable has a minimal impact on PPV, and its role in the modeling of PPV is not exact.

ACS Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Hoa Anh Nguyen; Dinh-An Nguyen; Le Thi Thu Hoa; Qui-Thao Le. Prediction of ground vibration intensity in mine blasting using the novel hybrid MARS–PSO–MLP model. Engineering with Computers 2021, 1 -19.

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran, Hoa Anh Nguyen, Dinh-An Nguyen, Le Thi Thu Hoa, Qui-Thao Le. Prediction of ground vibration intensity in mine blasting using the novel hybrid MARS–PSO–MLP model. Engineering with Computers. 2021; ():1-19.

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Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Hoa Anh Nguyen; Dinh-An Nguyen; Le Thi Thu Hoa; Qui-Thao Le. 2021. "Prediction of ground vibration intensity in mine blasting using the novel hybrid MARS–PSO–MLP model." Engineering with Computers , no. : 1-19.

Correction
Published: 16 February 2021 in Neural Computing and Applications
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The corrected version of Fig. 1 is given below.

ACS Style

Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Ngoc-Luan Mai. Correction to: A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Computing and Applications 2021, 1 -1.

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Hoang-Bac Bui, Ngoc-Luan Mai. Correction to: A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam. Neural Computing and Applications. 2021; ():1-1.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui; Hoang-Bac Bui; Ngoc-Luan Mai. 2021. "Correction to: A comparative study of artificial neural networks in predicting blast-induced air-blast overpressure at Deo Nai open-pit coal mine, Vietnam." Neural Computing and Applications , no. : 1-1.

Original paper
Published: 12 February 2021 in Natural Resources Research
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Blasting plays a fundamental role in rock fragmentation, and it is the first preparatory stage in the mining extraction process. However, its undesirable effects, mostly ground vibration, can cause severe damages to the surroundings, such as cracks/collapses of buildings, instability of slopes, deformation of underground space, affect underground water, to name a few. Therefore, the primary purpose of this study was to predict the intensity of ground vibration induced by mine blasting operations with high accuracy, aiming to reduce the severe damages to the surroundings. A novel artificial neural network (ANN)-based cuckoo search optimization (CSO), named as CSO–ANN model, was proposed for this aim based on 118 blasting events that were collected at a quarry mine in Vietnam. Besides, stand-alone models, such as ANN, support vector machine (SVM), tree-based ensembles, and two empirical equations (i.e., USBM and Ambraseys), were considered and developed for comparative evaluation of the performance of the proposed CSO–ANN model. Afterwards, they were tested and validated based on three blasting events in practical engineering. The results revealed that the CSO algorithm significantly improved the performance of the ANN model. In addition, the comparative results showed that the accuracy of the proposed hybrid CSO–ANN model was superior to the other models with MAE (mean absolute error) of 0.178, RMSE (root-mean-squared error) of 0.246, R2 (square of the correlation coefficient) of 0.990, VAF (variance accounted for) of 98.668, and a20-index of 1.0. Meanwhile, the other models only yielded performances in the range of 0.257–0.652 for RMSE, 0.932–0.987 for R2, 20.942–98.542 for VAF and 0.227–0.955 for a20-index. The findings also indicated that explosive charge per borehole has a special relationship with ground vibration intensity. It should be considered and used instead of total explosive charge per blast in some cases, especially for the empirical models.

ACS Style

Xuan-Nam Bui; Hoang Nguyen; Quang-Hieu Tran; Dinh-An Nguyen; Hoang-Bac Bui. Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization. Natural Resources Research 2021, 30, 2663 -2685.

AMA Style

Xuan-Nam Bui, Hoang Nguyen, Quang-Hieu Tran, Dinh-An Nguyen, Hoang-Bac Bui. Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization. Natural Resources Research. 2021; 30 (3):2663-2685.

Chicago/Turabian Style

Xuan-Nam Bui; Hoang Nguyen; Quang-Hieu Tran; Dinh-An Nguyen; Hoang-Bac Bui. 2021. "Predicting Ground Vibrations Due to Mine Blasting Using a Novel Artificial Neural Network-Based Cuckoo Search Optimization." Natural Resources Research 30, no. 3: 2663-2685.

Original paper
Published: 04 February 2021 in Natural Resources Research
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In this study, a coupling of generalized linear modeling (GLMNET) and nonlinear neural network modeling with multilayer perceptrons (MLPNN), called GLMNETs–MLPNN modeling, was conducted for predicting air over-pressure (AOp) induced by blasting in open-pit mines. Accordingly, six GLMNET models were developed first. Then, their predictions were bootstrap aggregated as the new predictors, and an optimal MLPNN model was developed based on these new predictors. To prove the improvement of the proposed GLMNETs–MLPNN model, the conventional models, such as GLMNET, support vector machine, MLPNN, random forest, and empirical, were considered and developed based on the same dataset. The results of the proposed model then were compared with that of the conventional models in terms of accurate prediction and modeling. The findings revealed that the bootstrap aggregating of six generalized linear models (i.e., GLMNET models) by a nonlinear model (i.e., MLPNN) could enhance the accuracy in predicting AOp with a root-mean-squared error (RMSE) of 2.266, determination coefficient (R2) of 0.916, and mean squared error (MAE) of 1.718. In contrast, the other stand-alone models provided poorer performances with RMSE of 2.981–4.686, R2 of 0.597–0.860, and MAE of 3.156–1.990. Besides, the sensitivity analysis results indicated that burden, stemming, distance, spacing and maximum explosive charge per delay were the most important parameters in predicting AOp.

ACS Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran. Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs–MLPNN). Natural Resources Research 2021, 1 -18.

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran. Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs–MLPNN). Natural Resources Research. 2021; ():1-18.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran. 2021. "Estimating Air Over-pressure Resulting from Blasting in Quarries Based on a Novel Ensemble Model (GLMNETs–MLPNN)." Natural Resources Research , no. : 1-18.

Erratum
Published: 30 January 2021 in Applied Soft Computing
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Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Ngoc-Luan Mai. Corrigendum to “A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms” [Appl. Soft Comput. 77 (2019) 376–386]. Applied Soft Computing 2021, 100, 107123 .

AMA Style

Hoang Nguyen, Xuan-Nam Bui, Quang-Hieu Tran, Ngoc-Luan Mai. Corrigendum to “A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms” [Appl. Soft Comput. 77 (2019) 376–386]. Applied Soft Computing. 2021; 100 ():107123.

Chicago/Turabian Style

Hoang Nguyen; Xuan-Nam Bui; Quang-Hieu Tran; Ngoc-Luan Mai. 2021. "Corrigendum to “A new soft computing model for estimating and controlling blast-produced ground vibration based on hierarchical K-means clustering and cubist algorithms” [Appl. Soft Comput. 77 (2019) 376–386]." Applied Soft Computing 100, no. : 107123.