Prediction of Soil Erosion Loss Mass in the Coal Mining Areas of Jilin Province Based on 3S Technology and BP Neural Network

2011 ◽  
Vol 225-226 ◽  
pp. 1246-1249
Author(s):  
Jie Tang ◽  
Yao Ji
Keyword(s):  
Neural Network ◽  
Soil Erosion ◽  
Coal Mining ◽  
Bp Neural Network ◽  
Jilin Province ◽  
Mining Areas ◽  
3S Technology ◽  
Erosion Index ◽  
Erosion Loss ◽  
Rainfall Erosion

This paper partitioned five major coal mining areas respectively in central, southern and eastern Jilin Province for case study based on current situation of exploitation and distribution of coal resources through artificial neural network(ANN) and the 3S technology to gain soil erosion loss mass. On the basis of RUSLE equation, BP neural network is fused to gain the rainfall erosion index of higher precision than those of traditional method. By extracting of indices and raster calculation on the platform of ERDAS and ArcGIS software, we made predication of soil erosion loss of the coal mining areas. After verification, the precision of rainfall erosion index is high, and thus improved the predicting accuracy of soil erosion. Comparative analysis shows that the soil erosion in central section of Jilin Province has much lower intensity, and high degree erosion occurred in the east and south mostly.

Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network

2015 ◽  
Vol 713-715 ◽  
pp. 1939-1942
Author(s):  
Xing Mei Xu ◽  
Li Ying Cao ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Grain Yield ◽  
Bp Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Jilin Province ◽  
Average Error ◽  
Main Factors

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

Prediction and Governance of Mine Gas Emission

2013 ◽  
Vol 419 ◽  
pp. 500-504
Author(s):  
Yi Wen Liu ◽  
Yi Cao ◽  
Lin Zhang ◽  
Ming Chuan Meng
Keyword(s):  
Neural Network ◽  
Coal Mining ◽  
Bp Neural Network ◽  
Emission Control ◽  
Control Measures ◽  
Gas Emission ◽  
Emission Data ◽  
Main Factors ◽  
Mine Gas ◽  
Emission Control Measures

Coal mining gas emission constrained by many factors, considering the eight main factors of gas emission. The first gas emission data are normalized, avoid data overflow to improve the training speed of neural network. Then use BP neural network to predict the amount of mine gas emission, finally proposed gas emission control measures.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

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