Research on early warning model of coal spontaneous combustion based on interpretability

Predicting the temperature of the coal spontaneous combustion (CSC) is essential for preventing and managing coal mine fires. In this paper, a Rough Set-Stacking-SHapley Additive Explanations (RS-Stacking-SHAP) prediction model of CSC based on grid search optimized is proposed. Compared with the tra...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 18847 - 16
Hauptverfasser:	Zhao, Huimin, Zhou, Xu, Han, Jingjing, Liu, Yixuan, Liu, Zhe, Wang, Shishuo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 29.05.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/166 639/705 Air leakage Algorithms Artificial intelligence Coal Coal mines Coal mining Coal spontaneous combustion Combustion Feature interaction interpretation Gases Humanities and Social Sciences Machine learning Methane multidisciplinary Oxidation Prediction models Rough set algorithm Science Science (multidisciplinary) SHAP Stacking Stacking integration architecture Temperature SHAP Feature interaction interpretation Coal spontaneous combustion Rough set algorithm Stacking integration architecture
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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