Predicting short-term rockburst intensity using a weighted probability stacking model with optimal feature selection and Bayesian hidden layer-Reference-Cited by-同舟云学术

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Predicting short-term rockburst intensity using a weighted probability stacking model with optimal feature selection and Bayesian hidden layer

Published:2024-11 Issue: Volume:153 Page:106021
ISSN:0886-7798
Container-title:Tunnelling and Underground Space Technology
language:en
Short-container-title:Tunnelling and Underground Space Technology

Author:

Sun Jiahao,Wang Wenjie^ORCID,Xie Lianku

Publisher

Elsevier BV

Reference68 articles.

1. Knowledge-based and data-driven fuzzy modeling for rockburst prediction;Adoko;Int. J. Rock Mech. Min. Sci.,2013

2. Some new Q-value correlations to assist in site characterisation and tunnel design;Barton;Int. J. Rock Mech. Min. Sci.,2002

3. Rockbursts: case studies from North American hard-rock mines;Blake;SME.,2003

4. Bagging predictors;Breiman;Mach. Learn.,1996

5. SMOTE: synthetic minority over-sampling technique;Chawla;J. Artif. Intell. Res.,2002

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