Investigation and application of data balancing and combined discriminant model in rock burst severity prediction

Investigation and application of data balancing and combined discriminant model in rock burst severity prediction

Abstract In the development of intelligent rock burst prediction models, issues such as incomplete data coverage and data imbalance are frequently encountered. These issues may lead to risks of overfitting in predictive models, poor generalization capabilities, and increased bias, which in turn may...

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Bibliographic Details
Main Authors: Shaohong Yan, Runze Liu, Yanbo Zhang, Xulong Yao, Yueqi Yang, Qi Wang, Bin Guo, Shuai Wang
Format: Article
Language:English
Published: Nature Portfolio 2024-11-01
Series:Scientific Reports
Subjects:
Rock burst intensity classification prediction
DBNR-Tomek Link sampling method
Bayesian optimization
Multi-layer perceptron
Random forest
Combination judgment prediction model
Online Access:https://doi.org/10.1038/s41598-024-81307-z
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https://doi.org/10.1038/s41598-024-81307-z

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