scholarly journals Constructing a Gas Explosion Inversion Model in a Straight Roadway Using the GA–BP Neural Network

ACS Omega ◽  
2021 ◽  
Author(s):  
Bo Tan ◽  
Heyu Zhang ◽  
Gang Cheng ◽  
Yanling Liu ◽  
Xuedong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gas Explosion ◽  
Inversion Model

scholarly journals Constructing a Gas Explosion Inversion Model in a Straight Roadway Using GA-BP Neural Network

2021 ◽  
Author(s):  
Bo Tan ◽  
Heyu Zhang ◽  
Gang Cheng ◽  
Yanling Liu ◽  
Xuedong Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Source Location ◽  
Gas Explosion ◽  
Accident Investigation ◽  
Rescue Work ◽  
Inversion Model ◽  
Emergency Rescue ◽  
Data Inversion

Abstract When the location and intensity of the source of the explosion is determined, the severity and impact of the explosion can be analyzed and predicted, such as the overpressure, temperature, and toxic gas propagation. In order to provide the theory of emergency rescue work, improve rescue efficiency, to protect the safety of rescue personnel. In addition, to determine the gas explosion source location and intensity of the accident investigation also has an important role, on the one hand,it helps to determine the accident-related responsible person, on the other hand,it also can more accurately judge the nature of the accident and the cause of the explosion, summarize the accident experience, for the future prevention of such accidents provide guidance experience. Therefore, the location and intensity of the source of the explosion through the field data inversion is of great significance. Based on Genetic Algorithm (GA, and similarly hereinafter) to improve the back propagation(BP) neural network theory, the use of the method through the gas explosion experiments and simulation of overpressure data inversion of roadway gas explosion source location and intensity, the establishment of roadway gas explosion disaster inversion model for emergency rescue and accident investigation to provide data support.

scholarly journals Inland Reservoir Water Quality Inversion and Eutrophication Evaluation Using BP Neural Network and Remote Sensing Imagery: A Case Study of Dashahe Reservoir

Water ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 2844
Author(s):  
Yanhu He ◽  
Zhenjie Gong ◽  
Yanhui Zheng ◽  
Yuanbo Zhang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Water Quality ◽  
Bp Neural Network ◽  
Quality Parameters ◽  
Remote Sensing Images ◽  
Linear Inversion ◽  
Reservoir Water ◽  
Chl A ◽  
Inversion Model

In this study, an inland reservoir water quality parameters’ inversion model was developed using a back propagation (BP) neural network to conduct reservoir eutrophication evaluation, according to multi-temporal remote sensing images and field observations. The inversion model based on the BP neural network (the BP inversion model) was applied to a large inland reservoir in Jiangmen city, South China, according to the field observations of five water quality parameters, namely, Chlorophyl-a (Chl-a), Secchi Depth (SD), total phosphorus (TP), total nitrogen (TN), and Permanganate of Chemical Oxygen Demand (CODMn), and twelve periods of Landsat8 satellite remote sensing images. The reservoir eutrophication was evaluated. The accuracy of the BP inversion model for each water parameter was compared with that of the linear inversion model, and the BP inversion models of two parameters (i.e., Chl-a and CODMn) with larger fluctuation range were superior to the two multiple linear inversion models due to the ability of improving the generalization of the BP neural network. The Dashahe Reservoir was basically in the state of mesotrophication and light eutrophication. The area of light eutrophication accounted for larger proportions in spring and autumn, and the reservoir inflow was the main source of nutrient salts.

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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