Prediction of Blast-Induced Ground Vibration in a Mine Using Relevance Vector Regression Optimized by Metaheuristic Algorithms

Natural Resources Research - Prediction of ground vibration induced by blasting operations is a crucial challenge to engineers working in surface mines. This study aims to assess the efficiency of...     

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 - In surface mining, blasting is an indispensable method for fragmenting rock masses. Nevertheless, it can inherently induce many side effects like ground vibrations. At...     

Prediction of Blast-Induced Rock Movement During Bench Blasting: Use of Gray Wolf Optimizer and Support Vector Regression

Natural Resources Research - A large ore loss and dilution can be expected when using a pre-blast ore boundary for shovel guidance because of the movement and re-distribution of ore in the muck...     

Prediction of Rock Size Distribution in Mine Bench Blasting Using a Novel Ant Colony Optimization-Based Boosted Regression Tree Technique

Natural Resources Research - In this paper, we used artificial intelligence (AI) techniques to investigate the relation between the rock size distribution (RSD) and blasting parameters for rock...     

Novel Extreme Learning Machine-Multi-Verse Optimization Model for Predicting Peak Particle Velocity Induced by Mine Blasting

Natural Resources Research - Peak particle velocity (PPV) is an important criterion for assessing the risk level of ground vibration induced by mine blasting. Based on this criterion, many efforts...     

Evaluation and Optimization of Prediction of Toe that Arises from Mine Blasting Operation Using Various Soft Computing Techniques

Natural Resources Research - Drilling and blasting operations are one of the most effective techniques for rock removal in mines. However, these operations are associated with some environmental...     

Prediction of Dust Emission Due to Open Pit Mine Blasting Using a Hybrid Artificial Neural Network

Natural Resources Research - This study developed a new perspective of artificial neural networks using dimensional analysis to be applicable to certain prediction problems. To this end,...     

Predicting Blast-Induced Ground Vibration in Open-Pit Mines Using Different Nature-Inspired Optimization Algorithms and Deep Neural Network

Natural Resources Research - Blast-induced ground vibration (GV) is a hazardous phenomenon in open-pit mines, and it has unquestionable effects, such as slope instability, deformation of...     

Prediction of Flyrock in Mine Blasting: A New Computational Intelligence Approach

Natural Resources Research - Blasting is the predominant rock fragmentation technique in civil constructions, underground and surface mines. Flyrock is the unwanted throw of rock fragments during...     

Goodnews Bay Platinum Resource Estimation Using Least Squares Support Vector Regression with Selection of Input Space Dimension and Hyperparameters

Resource estimation of a placer deposit is always a difficult and challenging job because of high variability in the deposit. The complexity of resource estimation increases when drill-hole data are sparse. Since sparsely sampled placer deposits produce high-nugget variograms, a traditional geostatistical technique like ordinary kriging sometimes fails to produce satisfactory results. In this article, a machine learning algorithm—the support vector machine (SVM)—is applied to the estimation of a platinum placer deposit. A combination of different neighborhood samples is selected for the input space of the SVM model. The trade-off parameter of the SVM and the bandwidth of the kernel function are selected by genetic algorithm learning, and the algorithm is tested on a testing data set. Results show that if eight neighborhood samples and their distances and angles from the estimated point are considered as the input space for the SVM model, the developed model performs better than other configurations. The proposed input space-configured SVM model is compared with ordinary kriging and the traditional SVM model (location as input) for resource estimation. Comparative results reveal that the proposed input space-configured SVM model outperforms the other two models.     

ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting

Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.

     

Artificial Neural Network and Firefly Algorithm for Estimation and Minimization of Ground Vibration Induced by Blasting in a Mine

Natural Resources Research - It is of a high importance to introduce intelligent systems for estimation and optimization of blasting-induced ground vibration because it is one the most unwanted...     

A Novel Multiple-Kernel Support Vector Regression Algorithm for Estimation of Water Quality Parameters

Natural Resources Research - The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high...     

Intelligent Prediction of Blasting-Induced Ground Vibration Using ANFIS Optimized by GA and PSO

Ground vibration induced by rock blasting is one of the most crucial problems in surface mines and tunneling projects. Hence, accurate prediction of ground vibration is an important prerequisite in the minimization of its environmental impacts. This study proposes hybrid intelligent models to predict ground vibration using adaptive neuro-fuzzy inference system (ANFIS) optimized by particle swarm optimization (PSO) and genetic algorithms (GAs). To build prediction models using ANFIS, ANFIS–GA, and ANFIS–PSO, a database was established, consisting of 86 data samples gathered from two quarries in Iran. The input parameters of the proposed models were the burden, spacing, stemming, powder factor, maximum charge per delay (MCD), and distance from the blast points, while peak particle velocity (PPV) was considered as the output parameter. Based on the sensitivity analysis results, MCD was found as the most effective parameter of PPV. To check the applicability and efficiency of the proposed models, several traditional performance indices such as determination coefficient (R²) and root-mean-square error (RMSE) were computed. The obtained results showed that the proposed ANFIS–GA and ANFIS–PSO models were capable of statistically predicting ground vibration with excellent levels of accuracy. Compared to the ANFIS, the ANFIS–GA model showed an approximately 61% decrease in RMSE and 10% increase in R². Also, the ANFIS–PSO model showed an approximately 53% decrease in RMSE and 9% increase in R² compared to ANFIS. In other words, the ANFIS performance was optimized with the use of GA and PSO.

     

运用小波变换与支持向量机耦合模型(WA-SVM)预测干旱区地下水埋深

准确预测干旱区地下水埋深,对区域地下水资源的合理开发利用与生态环境保护具有十分重要的意义。以额济纳盆地3个地下水埋深观测井为对象,运用小波变换与支持向量机耦合模型（WA-SVM）对观测井未来1个月的地下水埋深进行了短期预测。为检验WA-SVM的有效性,将模拟结果与未经小波变换的SVM模型进行了对比。结果表明：在对干旱区地下水埋深进行短期预测时,相较于SVM模型,WA-SVM模型的预测精度显著提高。WA-SVM模型在干旱区地下水埋深预测中有更好的适用性,可以为干旱地区地下水埋深动态预测提供新的方法和思路,是资料有限的条件下地下水埋深预测的有效方法。     

A Novel Hunger Games Search Optimization-Based Artificial Neural Network for Predicting Ground Vibration Intensity Induced by Mine Blasting

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.

     

基于地理加权回归模型的典型山地卫星反演降水产品降尺度研究

降水是陆地水循环的关键变量,高分辨率降水数据的获取是准确模拟陆地水循环过程的前提。虽然卫星反演降水产品具有较强的空间代表性和连续性,但其空间分辨率较低的问题限制了它的应用。以太行山、横断山和喀斯特山区为研究对象,基于降水与高程(DEM)、植被指数(NDVI)之间存在较好相关关系的假设,构建了GPM降水(Global Precipitation Measurement Mission)与高程、植被指数的地理加权回归模型,得到了2014—2016年研究区1km分辨率GPM降水数据。研究结果表明:地理加权回归模型能有效地提高GPM数据的空间分辨率。降尺度后,GPM数据精度在太行山和横断山区略有提高。年尺度上,相比于原始GPM数据,太行山和横断山区降尺度数据站点实测数据的确定系数分别提高了0.06和0.08,RMSE分别降低了0.45%和3.89%,MAE分别降低了0.16%和1.70%;月尺度上,太行山区67%的月份,横断山区83%的月份GPM产品降尺度后更加接近于站点实测数据。喀斯特地区GPM数据降尺度后精度略有下降,降尺度后,年尺度的降雨数据与实测数据的RMSE和MAE分别增加了10.00%和8.00%,R^2降低了0.06,月尺度上仅8月和9月降尺度后的精度更高。降雨与地形和NDVI的关系较弱是喀斯特地区降尺度效果较差的主要原因。     

Prediction of Blast-Induced Ground Vibration in an Open-Pit Mine by a Novel Hybrid Model Based on Clustering and Artificial Neural Network

Natural Resources Research - Ground vibration (PPV) is one of the hazard effects induced by blasting operations in open-pit mines, which can affect the surrounding structures, particularly the...     

Application of Tree-Based Predictive Models to Forecast Air Overpressure Induced by Mine Blasting

Natural Resources Research - In surface mines and underground excavations, every blasting operation can have some destructive environmental impacts, among which air overpressure (AOp) is of major...     

Computational Intelligence Model for Estimating Intensity of Blast-Induced Ground Vibration in a Mine Based on Imperialist Competitive and Extreme Gradient Boosting Algorithms

Natural Resources Research - In this paper, we developed a novel hybrid model ICA–XGBoost for estimating blast-produced ground vibration in a mine based on extreme gradient boosting (XGBoost)...