Rockburst Interpretation by a Data-Driven Approach: A Comparative Study
Accurately evaluating rockburst intensity has attracted much attention in these recent years, as it can guide the design of engineering in deep underground conditions and avoid injury to people. In this study, a new ensemble classifier combining a random forest classifier (RF) and beetle antennae se...
Saved in:
Main Authors: | Yuantian Sun, Guichen Li, Sen Yang |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/ee80cccec679496b970b2cd23a24d5e5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Research on Rockburst Prediction Classification Based on GA-XGB Model
by: Xuebin Xie, et al.
Published: (2021) -
Rockburst Precursors and the Dynamic Failure Mechanism of the Deep Tunnel: A Review
by: Yulong Chen, et al.
Published: (2021) -
Special issue: Informatics & data-driven medicine
by: Ivan Izonin, et al.
Published: (2021) -
An Improved Controlled Random Search Method
by: Vasileios Charilogis, et al.
Published: (2021) -
An Intelligent Rockburst Prediction Model Based on Scorecard Methodology
by: Honglei Wang, et al.
Published: (2021)