Earth Pressure Multipoint Prediction for EPS Shield Based on Multi-Model Ensemble

被引:0
|
作者
Zhang, Zheng [1 ]
Liu, Zhitao [1 ]
Su, Hongye [1 ]
Mao, Weijie [1 ]
Ma, Longhua [2 ]
机构
[1] Zhejiang Univ, State Key Lab Ind Control Technol, Inst Cyber Syst & Control, Hangzhou, Zhejiang, Peoples R China
[2] Zhejiang Univ, Ningbo Inst Technol, Coll Informat Sci & Engn, Ningbo, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
earth pressure; EPB; multi-model ensemble; Lasso; SVR; RFR; GBRT; feature importance; REGRESSION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The tunnel face stability is greatly influenced by the earth pressure in the chamber. It is of great practical significance to improve the performance of the earth pressure multipoint predictive model. In this study, a multi-model ensemble approach based on Lasso, Support Vector Regression, Random Forest and Gradient Boosting Decision Tree is presented for earth pressure multipoint prediction in EPB shield. The Leave-One-Out is adopted to validate the predictive performance. The feature importance is provided by the Lasso, Random Forest and Gradient Boosting Decision Tree model. The experimental results show that the performance of the multi-model ensemble is better than all single model.
引用
收藏
页码:1297 / 1302
页数:6
相关论文
共 50 条
  • [1] Model independence in multi-model ensemble prediction
    Abramowitz, Gab
    AUSTRALIAN METEOROLOGICAL AND OCEANOGRAPHIC JOURNAL, 2010, 59 : 3 - 6
  • [2] Multi-model ensemble wake vortex prediction
    Koerner, Stephan
    Holzaepfel, Frank
    AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY, 2016, 88 (02): : 331 - 340
  • [3] Decadal prediction skill in a multi-model ensemble
    Geert Jan van Oldenborgh
    Francisco J. Doblas-Reyes
    Bert Wouters
    Wilco Hazeleger
    Climate Dynamics, 2012, 38 : 1263 - 1280
  • [4] Decadal prediction skill in a multi-model ensemble
    van Oldenborgh, Geert Jan
    Doblas-Reyes, Francisco J.
    Wouters, Bert
    Hazeleger, Wilco
    CLIMATE DYNAMICS, 2012, 38 (7-8) : 1263 - 1280
  • [5] Mandarin Prosody Prediction Based on Attention Mechanism and Multi-model Ensemble
    Xie, Kun
    Pan, Wei
    INTELLIGENT COMPUTING THEORIES AND APPLICATION, PT I, 2018, 10954 : 491 - 502
  • [6] Multi-model Versus EPS-Based Ensemble of Atmospheric Dispersion Predictions: A Quantitative Assessment
    Galmarini, S.
    Potempski, S.
    Bonnardot, F.
    Jones, A.
    Robertson, L.
    AIR POLLUTION MODELING AND ITS APPLICATION XX, 2010, : 401 - 403
  • [7] MVL spatiotemporal analysis for model intercomparison in EPS: application to the DEMETER multi-model ensemble
    Fernandez, J.
    Primo, C.
    Cofino, A. S.
    Gutierrez, J. M.
    Rodriguez, M. A.
    CLIMATE DYNAMICS, 2009, 33 (2-3) : 233 - 243
  • [8] MVL spatiotemporal analysis for model intercomparison in EPS: application to the DEMETER multi-model ensemble
    J. Fernández
    C. Primo
    A. S. Cofiño
    J. M. Gutiérrez
    M. A. Rodríguez
    Climate Dynamics, 2009, 33 : 233 - 243
  • [9] Multi-model ensemble hydrologic prediction and uncertainties analysis
    Jiang, Shanhu
    Ren, Liliang
    Yang, Xiaoli
    Ma, Mingwei
    Liu, Yi
    EVOLVING WATER RESOURCES SYSTEMS: UNDERSTANDING, PREDICTING AND MANAGING WATER-SOCIETY INTERACTIONS, 2014, 364 : 249 - 254
  • [10] A Forest Fire Prediction Model Based on Meteorological Factors and the Multi-Model Ensemble Method
    Choi, Seungcheol
    Son, Minwoo
    Kim, Changgyun
    Kim, Byungsik
    FORESTS, 2024, 15 (11):