Improved mining subsidence prediction model for high water level area using machine learning and chaos theory

Yang, Xu; Chen, Xingda; Fang, Xinjian; Chi, Shenshen; Zhu, Mingfei

doi:10.1177/01445987221107679

“…This streamlined process empowers mining engineers to promptly make decisions regarding subsidence mitigation measures, safeguarding mining operations' safety and sustainability. These algorithms also exhibit adaptability, learning from new data inputs (Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In 2022, Yang and colleagues introduced an innovative land subsidence prediction model based on a multi-kernel support vector machine (GA-MK-SVM). This model was designed to enhance the mine subsidence prediction model by taking into account the underground water level (Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Jahanmiri,

Noorian-Bidgoli

2024

Environ Sci Pollut Res

2

0

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Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as troughs subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. We consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

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Research on risk assessment technology for transmission lines in mining affected areas

Lu,

Xu,

Wang

et al. 2024

AIP Advances

1

0

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This article studies the correlation between ground surface deformation of transmission lines in mining affected areas and various geological and human factors, and it constructs the corresponding numerical models. By analyzing the distribution of goaf areas, geological structures, slope structures, and other factors that affect transmission lines, the tower foundations’ deformation characteristics and disaster mechanisms under mining conditions have been clarified. On this basis, research on ground surface deformation risk prediction technology was carried out using monitoring data, and a safety operation risk assessment model was established to achieve rapid identification and accurate prediction and warning of hidden dangers. At the same time, a monitoring, early warning, and risk assessment system has been constructed and demonstrated to be highly accurate and practical in pilot verification. In addition, through the comprehensive application of physical models and numerical simulations, the influence of different ground surface settlements on the deformation of transmission line towers was explored, providing important references for improving the safety of transmission lines.

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Land Subsidence Prediction in Coal Mining Using Machine Learning Models and Optimization Techniques

jahanmiri,

Noorian-Bidgoli

2023

Preprint

0

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Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as troughs subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. We consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

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Improved mining subsidence prediction model for high water level area using machine learning and chaos theory

Cited by 5 publications

References 35 publications

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Land subsidence prediction in coal mining using machine learning models and optimization techniques

Research on risk assessment technology for transmission lines in mining affected areas

Land Subsidence Prediction in Coal Mining Using Machine Learning Models and Optimization Techniques

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