An Unsupervised Learning Approach for Coal Spontaneous Combustion Warning Level Classification Using t-SNE and k-Means Clustering

An Unsupervised Learning Approach for Coal Spontaneous Combustion Warning Level Classification Using t-SNE and k-Means Clustering

Accurate prediction of coal spontaneous combustion levels is crucial for preventing and controlling spontaneous combustion in goaf areas. To address the ambiguity in classification standards of coal spontaneous combustion warning levels, 21 groups of coal samples from different mining areas were sub...

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Bibliographic Details
Main Authors: Pengyu Zhang, Xiaokun Chen
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
Subjects:
coal spontaneous combustion
warning level
unsupervised learning
support vector classifier
classification model
Online Access:https://www.mdpi.com/2076-3417/15/7/3756
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