Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

Pengcheng Yan; Songhang Shang; Chaoyin Zhang; Nini Yin; Xiaofei Zhang; Gaokun Yang; Zhuang Zhang; Quansheng Sun

doi:10.1109/ACCESS.2021.3102020

IEEE Access (Jan 2021)

Research on the Processing of Coal Mine Water Source Data by Optimizing BP Neural Network Algorithm With Sparrow Search Algorithm

Pengcheng Yan,
Songhang Shang,
Chaoyin Zhang,
Nini Yin,
Xiaofei Zhang,
Gaokun Yang,
Zhuang Zhang,
Quansheng Sun

Affiliations

Pengcheng Yan: ORCiD; State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan, China
Songhang Shang: ORCiD; College of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, Huainan, China
Chaoyin Zhang: College of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, Huainan, China
Nini Yin: State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine, Anhui University of Science and Technology, Huainan, China
Xiaofei Zhang: College of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, Huainan, China
Gaokun Yang: College of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, Huainan, China
Zhuang Zhang: College of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, Huainan, China
Quansheng Sun: College of Electrical and Information Engineering, Anhui University of Science and Technology, Anhui, Huainan, China

DOI: https://doi.org/10.1109/ACCESS.2021.3102020
Journal volume & issue: Vol. 9
pp. 108718 – 108730

Abstract

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Coal mine safety is crucial to the healthy and sustainable development of the coal industry, and coal mine flood is a major hidden danger of coal mine accidents. Therefore, the processing of coal mine water source data is of great significance to prevent mine water inrush accidents. In this experiment, the water source data were obtained by laser induced fluorescence technology with the assistance of laser. The water sample data information was preprocessed by standard normal variable transformation (SNV) and multiple scattering correction (MSC), and then the principal component analysis (PCA) was used to reduce the dimension of the data and ensure the information characteristics of the original data unchanged. In order to identify the water inrush type of coal mine water source, the sparrow search algorithm (SSA) is used to optimize the BP neural network in this study. This is because the SSA algorithm has the advantages of strong optimization ability and fast convergence rate compared with particle swarm optimization (PSO) and other optimization algorithms. Experiments show that under the premise of SNV pretreatment, the R2 of SSA-BP model is infinitely close to 1, MRE is 0.0017, RMSE is 0.0001, the R2 of PSO-BP model is 0.9995, MRE is 0.0026, RMSE is 0.0019, the R2 of BP model is 0.9983, MRE is 0.0140, RMSE is 0.0075. Therefore, SSA-BP model is more suitable for the classification of coal mine water sources.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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