JACIII Vol.28 No.3 pp. 606-612
doi: 10.20965/jaciii.2024.p0606

Research Paper:

A Three-Zone Identification Method for Coal Mine Area Based on DS Evidence Theory

Yuqi Feng*1,*2,*3, Wangyong He*1,*2,*3,†, and Yun Liu*4

*1School of Automation, China University of Geosciences
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

*2Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

*3Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education
No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China

*4Qinghai Bureau of Environmental Geology Exploration
No.18 Wenjing Street, Chengxi District, Xining, Qinghai 810000, China

Corresponding author

March 30, 2023
January 29, 2024
May 20, 2024
stratigraphy identification, information fusion, BP neural network, DS evidence theory, three-zone

As coal ore and other resources are continuously mined, a three-zone structure is formed underground consisting of a sagging zone, fault zone, and caving zone. The use of well-logging data to identify the three zones is important for production safety and environmental management. Owing to the scarcity of data that can reflect three zones in normal coal mining, conventional identification and prediction methods face challenges when extracting data features, incurring a degree of uncertainty within prediction results. Accordingly, the accurate identification of the three zones has become a critical objective in daily production. To address this issue, we developed a method called a method called backpropagation neural networks with Dempster–Shafer (DS) evidence theory. Initially, we preprocessed the training data and deployed two backpropagation neural networks (BPNNs) to predict the three zones according to two parameters. According to these prediction results, the local and global credibility of each prediction is calculated and used to obtain the basic probability assignment function required for the DS evidence theory. Finally, the DS evidence theory is used to fuse the two BPNNs prediction results, thereby producing the final prediction results. The proposed method was demonstrated to improve prediction accuracy by 6.4% compared to a conventional neural network.

Cite this article as:
Y. Feng, W. He, and Y. Liu, “A Three-Zone Identification Method for Coal Mine Area Based on DS Evidence Theory,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.3, pp. 606-612, 2024.
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Last updated on Jun. 03, 2024