Tolerated outlier prediction method of excavation damaged zone thickness of drift based on interpretable SOA-QRF ensemble learning-Reference-Cited by-同舟云学术

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Tolerated outlier prediction method of excavation damaged zone thickness of drift based on interpretable SOA-QRF ensemble learning

Published:2024-04-11 Issue: Volume: Page:1-29
ISSN:1749-9518
Container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards
language:en
Short-container-title:Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards

Author:

Shen Yaxi¹,Wu Shunchuan¹²,Wang Yongbing³,Wang Jiaxin²,Wang Shuxian¹,Huang Shigui²

Affiliation:

1. Faculty of Public Safety and Emergency Management, Kunming University of Science and Technology, Kunming, People’s Republic of China

2. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming, People’s Republic of China

3. Huize Mining Company, Yunnan Chihong Zinc and Germanium Co., Ltd., Qujing, People’s Republic of China

Funder

Major Scientific and Technological Projects of Yunnan

Yunnan innovation team

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17499518.2024.2337387

Reference70 articles.

1. Nonlinear prediction of fuzzy regression model based on quantile loss function

2. Application of Seagull Optimization Algorithm Log Interpretation to Shale Gas Reservoir Of Well H in Sichuan Basin Yuxi Block;Chen Yuanyuan;Advance in Earth Sciences,2020

3. Predicting the Thickness of an Excavation Damaged Zone around the Roadway Using the DA-RF Hybrid Model

4. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

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