Risk assessment of floor water inrush in coal mines based on MFIM-TOPSIS variable weight model

Floor water inrush is one of the main types of coal mine water hazards. With the development of deep mining, the prediction and evaluation of floor water inrush is particularly significant. This paper proposes a variable weight model, which combines a multi-factor interaction matrix (MFIM) and the t...

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Bibliographic Details
Published in:Journal of Central South University 2021-08, Vol.28 (8), p.2360-2374
Main Authors: Zhang, Guan-da, Xue, Yi-guo, Bai, Cheng-hao, Su, Mao-xin, Zhang, Kai, Tao, Yu-fan
Format: Article
Language:English
Subjects:
Algorithms
Aquifers
Coal mines
Coal mining
Engineering
Metallic Materials
Mine waters
Mines
Risk assessment
Risk levels
Thickness
Water pressure
Water supply
Water yield
Weight
Work face
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Summary:Floor water inrush is one of the main types of coal mine water hazards. With the development of deep mining, the prediction and evaluation of floor water inrush is particularly significant. This paper proposes a variable weight model, which combines a multi-factor interaction matrix (MFIM) and the technique for order performance by similarity to ideal solution (TOPSIS) to implement the risk assessment of floor water inrush in coal mines. Based on the MFIM, the interaction between seven evaluation indices, including the confined water pressure, water supply condition and aquifer water yield property, floor aquifuge thickness, fault water transmitting ability, fracture development degree, mining depth and thickness and their influence on floor water inrush were considered. After calculating the constant weights, the active degree evaluation was used to assign a variable weight to the indices. The values of the middle layer and final risk level were obtained by TOPSIS. The presented model was successfully applied in the 9901 working face in the Taoyang Mine and four additional coal mines and the results were highly consistent with the engineering situations. Compared with the existing nonlinear evaluation methods, the proposed model had advantages in terms of the weighting, principle explanation, and algorithm structure.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-021-4775-x