Top corner gas concentration prediction using t‐distributed Stochastic Neighbor Embedding and Support Vector Regression algorithms

Summary The excess of gas concentration in the top corner of coal working face has always been the main factor restricting the safe productivity of coal mines. Therefore, the rapid and accurate prediction of top corner gas concentration is an effective method to prevent gas disasters. At the same ti...

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Vydané v:Concurrency and computation Ročník 32; číslo 14
Hlavní autori: Wu, Haibo, Shi, Shiliang, Lu, Yi, Liu, Yong, Huang, Weihong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Hoboken, USA John Wiley & Sons, Inc 25.07.2020
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Predmet:
Algorithms
Coal
Coal mines
Embedding
Feature extraction
gas concentration
Internet of Things
Mining industry
Monitoring
nonlinear dimensionality reduction
Occupational safety
prediction
Prediction models
Principal components analysis
Regression analysis
Regression models
Spatial data
Support vector machines
SVR
top corner
t‐SNE
ISSN:1532-0626, 1532-0634
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Shrnutí:Summary The excess of gas concentration in the top corner of coal working face has always been the main factor restricting the safe productivity of coal mines. Therefore, the rapid and accurate prediction of top corner gas concentration is an effective method to prevent gas disasters. At the same time, the development of the Internet of things has made the gas monitoring data collected by the coal mine safety monitoring system exhibit nonlinear big data characteristics. In order to mine the characteristic data related to the gas concentration of the top corner from a high‐dimensional and nonlinear monitoring datasets, a model that integrates the t‐distributed Stochastic Neighbor Embedding algorithm (t‐SNE) and the Support Vector Regression (SVR) algorithm to predict the gas concentration of the top corner on the coal working face is proposed. First, the multidimensional monitoring data are nonlinearly dimension‐reduced by t‐SNE algorithm, which enabled the spatial feature data of the monitoring data to be extracted. After that, the SVR algorithm was used to construct the nonlinear regression model between the spatial feature data and the actual gas concentration of the top corner to predict the gas concentration of the top corner. The experimental results show that the predictive model based on t‐SNE and SVR was better than the multiple linear regression, SVR, Principal Components Analysis (PCA) + SVR. The results show the model based on t‐SNE and SVR was more stable and could provide more accurate predictions, anomaly sensitivity, and the fitness is 0.55628405, which can better fit the actual gas concentration of the top corner.
Bibliografia:Funding information
National Natural Science Foundation of China, 51774135; 51974119; 51974120; Provincial Natural Science of Hunan, 2019JJ50512; Safety Production Special Foundation of Hunan Province, [2017]20; Teaching Reform Project of Common Colleges and Universities in Hunan Province, Hunan Province Key Laboratory of Coal Resources Clean‐utilization and Mine Environment, Project, 2017‐237; Hunan University of Science and Technology, E21701; 201304; Doctoral Scientific Research Foundation of Hunan University of Science and Technology project, E51771; Research Foundation of Education Bureau of Hunan Province, 18B210
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5705