Hydrogeological changes caused by opencast coal mining in steppe zone: a case study of Shengli 1 open-pit coal mine

2018 ◽  
Vol 121 ◽  
pp. 126-133
Author(s):  
Zhenguo Xing ◽  
Suping Peng ◽  
Wenfeng Du ◽  
Yunlan He ◽  
Shan Chong ◽  
...  
Keyword(s):  
Coal Mining ◽  
Coal Mine ◽  
Steppe Zone ◽  
Open Pit

Impacts of Coal Mining on the Aboveground Vegetation and Soil Quality: A Case Study of Qinxin Coal Mine in Shanxi Province, China

2011 ◽  
Vol 39 (3) ◽  
pp. 219-225 ◽  
Author(s):  
Guo Donggan ◽  
Bai Zhongke ◽  
Shangguan Tieliang ◽  
Shao Hongbo ◽  
Qiu Wen
Keyword(s):  
Coal Mining ◽  
Soil Quality ◽  
Coal Mine ◽  
Shanxi Province

Wongawilli roadway backfilling coal mining method - a case study in Wangtaipu coal mine

2019 ◽  
Vol 20 (3) ◽  
pp. 342 ◽  
Author(s):  
Liqiang Ma ◽  
Zhiyuan Jin ◽  
Wenpeng Liu ◽  
Dongsheng Zhang ◽  
Yao Zhang
Keyword(s):  
Coal Mining ◽  
Coal Mine ◽  
Mining Method

scholarly journals Recognition of Landscape Key Areas in a Coal Mine Area of a Semi-Arid Steppe in China: A Case Study of Yimin Open-Pit Coal Mine

2020 ◽  
Vol 12 (6) ◽  
pp. 2239 ◽  
Author(s):  
Shougang Wang ◽  
Jiu Huang ◽  
Haochen Yu ◽  
Chuning Ji
Keyword(s):  
Coal Mine ◽  
Open Pit ◽  
Ecological Security ◽  
Recognition Model ◽  
Security Pattern ◽  
Arid Steppe ◽  
Landscape Types ◽  
Total Study Area ◽  
Semi Arid

The ecological integrity and biodiversity of steppes were destroyed under the long-term and high-intensity development of open-pit coal mines in China, causing desertification, steppe degradation, landscape function defect, and so on. As a source of species maintenance and dispersal, an ecological source is a key area for preservation in order to restore the ecological security pattern of the larger landscape. The purpose of this study was to establish a landscape key area recognition model to identify the landscape key areas (LKA) surrounding an open pit coalmine located in semi-arid steppe. This study takes the Yimin open pit mining area as a case study. We assessed Landsat 5 Thematic Mapper (TM) and Landsat 8 Operational Land Imager (OLI) remote sensing images taken during the peak season of vegetation growth from July to August in 1999, 2006, 2011, and 2017. From these images, we identified the main landscape types and vegetation coverage grades in order to identify the ecological land. Next, we applied the three indices of Importance of Patch Connectivity, Habitat Quality, and Ecosystem Service Value to calculate the comprehensive results that identify ecological land. Finally, the ecological land quality results of different years are superimposed and averaged, and then Very Important Patch (VIMP), Important Patch (IMP), and General Patch (GEP) areas were used for LKA extraction. Our results showed LKA to cover 177.35 km2, accounting for 20.01% of the total study area. The landscape types identified as LKA are primarily grassland (47.37%), wetland (40.27%), and shrubland (11.88%), indicating that landscape type correlates strongly with its value as a landscape key area. The proposed landscape key area recognition model could enrich the foundations for ecological planning and ecological security pattern construction in order to support ecological protection and restoration in semi-arid steppe areas affected by coal mining.

scholarly journals Application of the k - nearest neighbors algorithm for predicting blast - induced ground vibration in open - pit coal mines: a case study

2020 ◽  
Vol 61 (6) ◽  
pp. 22-29
Author(s):  
Hoang Nguyen . ◽  
Keyword(s):  
Artificial Intelligence ◽  
Coal Mine ◽  
Empirical Model ◽  
Ground Vibration ◽  
Nearest Neighbors ◽  
Superior Performance ◽  
Open Pit ◽  
K Nearest Neighbors ◽  
Open Pit Mines

Blasting is considered as one of the most effective methods for rock fragmentation in open - pit mines. However, its side effects are significant, especially blast - induced ground vibration. Therefore, this study aims to develop and apply artificial intelligence in predicting blast - induced ground vibration in open - pit mines. Indeed, the k - nearest neighbors (KNN) algorithm was taken into account and developed for predicting blast - induced ground vibration at the Deo Nai open - pit coal mine (Vietnam) as a case study. An empirical model (i.e., USBM) was also developed to compare with the developed KNN model aiming to highlight the advantage of the KNN model. Accordingly, 194 blasting events were collected and analyzed for this aim. This database was then divided into two parts, 80% for training and 20% for testing. The MinMax scale and 10 - fold cross - validation techniques were applied to improve the accuracy, as well as avoid overfitting of the KNN model. Root - mean - squared error (RMSE) and determination coefficient (R2) were used as the performance metrics for models’ evaluation and comparison purposes. The results indicated that the KNN model yielded better superior performance than those of the USBM empirical model with an RMSE of 1.157 and R2 of 0.967. In contrast, the USBM model only provided a weak performance with an RMSE of 4.205 and R2 of 0.416. With the obtained results, the KNN can be introduced as a potential artificial intelligence model for predicting and controlling blast - induced ground vibration in practical engineering, especially at the Deo Nai open - pit coal mine.

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