Extraction of Open-PIT Mine Reclamation Area with Convolutional Neural Network

2021 ◽  
Author(s):  
Congtang Meng ◽  
Yindi Zhao ◽  
Bo Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Open Pit Mine ◽  
Mine Reclamation ◽  
Open Pit ◽  
Reclamation Area

scholarly journals An Automatic Monitoring Method of Slope Deformation in Open-pit mine based on BP Neural Network and GIS Technology

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rapid Development ◽  
Automatic Monitoring ◽  
Deformation Monitoring ◽  
Open Pit Mine ◽  
Slope Deformation ◽  
Open Pit ◽  
Gis Technology ◽  
Monitoring Method

At present, the research on BP neural network has achieved good results in many industries and fields, but there are few projects in the application research of mineral resources mining. Under the social background of the rapid development of electronic information technology, BP neural network and GIS technology are combined to carry out research and application, which will provide a new research path for slope deformation monitoring and disaster prevention in mining area. Therefore, in the paper, the key technology of open-pit mine slope deformation automatic monitoring based on BP neural network and GIS technology was put forward. Firstly, the advantages of BP neural network were analyzed and BP neural network was selected as the prediction model of slope deformation. The artificial fish swarm algorithm was used to improve the BP neural network to improve the performance of the model. Based on the analysis and construction of GIS technology, the combination application of BP neural network and GIS technology was discussed. Through practice, the application effect of the technology was verified, and it has good theoretical and practical value

scholarly journals TYPES OF AMPHIBIA IN COAL MINE RECLAMATION OF PT. BUKIT ASAM, Tbk. TANJUNG ENIM, SOUTH SUMATRA.

2021 ◽  
Vol 7 (1) ◽  
pp. 18-24
Author(s):  
Nur Hayati ◽  
Yuanita Windusari ◽  
Zulkifli Dahlan
Keyword(s):  
Coal Mining ◽  
Coal Mine ◽  
Environmental Conditions ◽  
Negative Impact ◽  
Mine Reclamation ◽  
Open Pit ◽  
Survey Method ◽  
Mining Activities ◽  
Reclamation Area ◽  
Coal Mine Reclamation

Coal mining activities in South Sumatra are among others carried out by PT. Bukit Asam tbk, a coal company located in Tanjung Enim. The open pit/cast mining process has a negative impact on the environment, some of the impacts are land degradation, loss of vegetation, changes in microclimate and loss of biodiversity which includes diversity of flora and fauna, one of which is amphibians. One of the efforts to maintain and preserve environmental capabilities is to reclaim former mines. In accordance with Law No. 4 of 2009 concerning Mineral and Coal Mining requires mining companies to carry out reclamation and post-mining activities over the areas they cultivate. Reclamation-revegetation activities are efforts to improve microclimate conditions, improve soil fertility conditions. The recovery of environmental conditions is expected to bring back the wildlife that has been lost. The amphibian community is believed to be a bioindicator of the recovery of environmental conditions undergoing habitat changes. The research was carried out at the Bukit Asam coal mine reclamation area, Air laya site from February to March. The aim of the study was to see the types of amphibians found in the mine reclamation area. Sampling was carried out using the VES (Visual Enconter Survey) method. From the results obtained, there were 3 species from 2 members of the Order (Anura) and 2 members of the Family (Rinidae).

scholarly journals Development of a predictive model of rock fragmentation for Nui Phao open-pit mine in Vietnam using multiple-output Neural Networks and Monte Carlo Dropout technique

2021 ◽  
Author(s):  
Trong Vu ◽  
Tran Bao
Keyword(s):  
Neural Network ◽  
Monte Carlo ◽  
Predictive Model ◽  
Network Models ◽  
Open Pit Mine ◽  
Open Pit ◽  
Neural Network Models ◽  
Mining Operations ◽  
Single Output ◽  
Blast Fragmentation

Abstract Precise and reliable prediction of blast fragmentation is essential for the success of mining operations. Over the years, various machine learning models using artificial neural network have developed and proven to be efficient in predicting the blast fragmentation. In this research, we design multiple-output neural networks to forecast the cumulative distribution function (CDF) of blast fragmentation to improve this prediction. The model architecture contains multiple response variables in the output layer that correspond to the CDF curve’s percentiles. We apply Monte Carlo dropout procedure to estimate the uncertainty of the predictions. Data collected from a Nui Phao open-pit mine in Vietnam are used to train and validate the performance of the model. Results suggest that multiple-output neural network models provide better accuracy than single-output neural network models that predict each percentile on a CDF independently. Whereas, Monte Carlo dropout technique can give valuable and relative reliable information during decision making. Article highlights: • Precise and reliable prediction of blast fragmentation is essential for the success of mining operations. • A predictive model based on the multi-output neural network and Monte Carlo dropout technique was designed to predict the fragmentation CDF curve in the blasting operation of an open-pit mine. • The predictive model was proven reliable and provided better accuracy than models based on a single-output neural network.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network
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