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...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Stochastic environmental research and risk assessment Ročník 38; číslo 5; s. 1701 - 1720
Hlavní autoři:	Wang, Shichao, Zhai, Peihe, Yu, Xiaoge, Han, Jin, Shi, Longqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024 Springer Nature B.V
Témata:	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
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	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 model, through a unique autoencoder framework and a custom ‘masked_loss’ loss function, achieves semi-supervised learning and dimensionality reduction of groundwater sample ionic data. By configuring the hidden layers, the classifier component of the model directly receives data processed by the encoder component. This not only improves the model's performance but also optimizes its complexity. Through an evaluation of the model's fitting effectiveness, our model achieved an average accuracy of 88.8% across 20 runs, with precision, recall, F1-score, and MCC reaching 88.1%, 80.6%, 0.827, and 0.833, respectively, significantly outperforming other classic models. The model successfully identified the sources of three sets of inrush water samples, with a high number of successful runs and clear average probabilities. This work contributes not only to the field of mine water inrush source identification but also offers a new perspective for the broader field of machine learning.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1436-3240 1436-3259
DOI:	10.1007/s00477-023-02647-2