Optimizing identification of mine water inrush source with manifold reduction and semi-supervised learning using improved autoencoder

To enhance the accuracy of identifying water sources in mine inrush incidents, this study, taking the Shengquan coal mine in Shandong, China, as a case study, proposes a novel water source identification model based on an improved autoencoder—the “Masked Autoencoder-based Classifier” model. This mod...

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Veröffentlicht in:	Stochastic environmental research and risk assessment Jg. 38; H. 5; S. 1701 - 1720
Hauptverfasser:	Wang, Shichao, Zhai, Peihe, Yu, Xiaoge, Han, Jin, Shi, Longqing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024 Springer Nature B.V
Schlagworte:	Accuracy Aquatic Pollution Aquifers Case studies Chemistry and Earth Sciences Classifiers Coal mines Coal mining Computational Intelligence Computer Science Earth and Environmental Science Earth science Earth Sciences Environment Environmental research Groundwater Groundwater data Identification Machine learning Math. Appl. in Environmental Science Mine drainage Mine waters Neural networks Original Paper Physics Principal components analysis Probability Theory and Stochastic Processes Risk assessment Semi-supervised learning Statistics for Engineering Waste Water Technology Water analysis Water inrush Water Management Water Pollution Control Water sampling Water source identification Semi-supervised learning Autoencoder Manifold reduction Mine water
ISSN:	1436-3240, 1436-3259
Online-Zugang:	Volltext
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