Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest

Interpretable model for rockburst intensity prediction based on Shapley values-based Optuna-random forest

To address the limitation of traditional machine learning models in explaining the rockburst intensity prediction process, this study proposes an interpretable rockburst intensity prediction model. The model was developed using 350 sets of actual rockburst sample data to explore the impact of input...

Full description

Saved in:
Bibliographic Details
Main Authors: Yaxi Shen, Shunchuan Wu, Yongbing Wang, Jiaxin Wang, Zhiquan Yang
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-04-01
Series:Underground Space
Subjects:
Rockburst intensity
Isolation forest
Synthetic minority oversampling
Random forest
Interpretable model
Online Access:http://www.sciencedirect.com/science/article/pii/S2467967424001089
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

http://www.sciencedirect.com/science/article/pii/S2467967424001089

Similar Items