Ensemble stacking rockburst prediction model based on Yeo–Johnson, K-means SMOTE, and optimal rockburst feature dimension determination

Rockburst forecasting plays a crucial role in prevention and control of rockburst disaster. To improve the accuracy of rockburst prediction at the data structure and algorithm levels, the Yeo–Johnson transform, K-means SMOTE oversampling, and optimal rockburst feature dimension determination are use...

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Detalles Bibliográficos
Autores principales: Sun, Lijun, Hu, Nanyan, Ye, Yicheng, Tan, Wenkan, Wu, Menglong, Wang, Xianhua, Huang, Zhaoyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468028/
https://www.ncbi.nlm.nih.gov/pubmed/36097043
http://dx.doi.org/10.1038/s41598-022-19669-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9468028/
https://www.ncbi.nlm.nih.gov/pubmed/36097043
http://dx.doi.org/10.1038/s41598-022-19669-5

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