AIMC Topic: Coal Mining

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Research on missing value prediction of measured ERT data for coal mine based on a GRNN algorithm.

PloS one
In the process of long-term monitoring of the coal seam floor of a coal mining face using electrical resistivity tomography (ERT), the data loss caused by electrode disconnection adversely affects early warning of water inrush and prevents the identi...

Research on computational propagation and identification of mine microseismic signals based on deep learning.

PloS one
In the mining field, hydraulic fracturing of coal - seam boreholes generates a large number of weak microseismic signals. The accurate identification of these signals is crucial for subsequent positioning and inversion. However, when dealing with suc...

SHAP-driven insights into multimodal data: behavior phase prediction for industrial safety applications.

Scientific reports
Unsafe behaviors among coal miners are a primary factor contributing to accidents, posing significant challenges for safety management. This study develops a behavior state prediction framework using artificial intelligence and machine learning (ML) ...

High-precision deformation monitoring and intelligent early warning for wellbore based on BDS/GNSS.

PloS one
To address the complex deformation of wellbores influenced by surrounding coal mining operations, this study employed an improved modified least-squares ambiguity decorrelation (MLAMBDA) algorithm based on the double-difference model for high-frequen...

Evaluation of machine learning models for accurate prediction of heavy metals in coal mining region soils in Bangladesh.

Environmental geochemistry and health
Coal mining soils are highly susceptible to heavy metal pollution due to the discharge of mine tailings, overburden dumps, and acid mine drainage. Developing a reliable predictive model for heavy metal concentrations in this region has proven to be a...

Early warning of deep coal miners' unsafe behavior based on the HFACS-CM-BP neural network.

International journal of occupational safety and ergonomics : JOSE
Preventing miners' unsafe behavior and reducing accidents in deep coal mines are crucial. This study comprehensively used methods such as the human factor analysis and classification system for China mines (HFACS-CM) model, grounded theory and the ba...

Coal and gas outburst prediction based on data augmentation and neuroevolution.

PloS one
Coal and gas outburst (CGO) is a complicated natural disaster in underground coal mine production. In constructing smart mines, predicting CGO risks efficiently and accurately is necessary. This paper proposes a CGO risk prediction method based on da...

A spatial machine learning approach to exploring the impacts of coal mining and ecological restoration on regional ecosystem health.

Environmental research
Ecosystem health is an important approach to measuring urban and regional sustainability. In previous studies, the spatiotemporal changes of ecosystem health have been addressed using comprehensive assessment index system. However, the quantitative c...

Research on state perception of scraper conveyor based on one-dimensional convolutional neural network.

PloS one
Addressing the challenges of current scraper conveyor health assessments being influenced by expert knowledge and the relative difficulty in establishing degradation models for equipment, this study proposed a method for assessing the health status o...

Neuro-fuzzy prediction model of occupational injuries in mining.

International journal of occupational safety and ergonomics : JOSE
This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neural networks and fuzzy logic theory. Accidents are common due to the unique nature of underground mi...