A Deformation Forecasting Model of High and Steep Slope Based on Fuzzy Time Series and Entire Distribution Optimization

被引:2
|
作者
Fu, Yanhua [1 ]
Wan, Lushan [2 ]
Fu, Xiaorui [2 ]
Xiao, Dong [2 ]
Mao, Yachun [3 ]
Sun, Xiaoyu [3 ]
机构
[1] Northeastern Univ, Jangho Architecture Coll, Shenyang 110169, Peoples R China
[2] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
[3] Northeastern Univ, Sch Resources & Civil Engn, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
Time series analysis; Strain; Optimization; Sociology; Predictive models; Azimuth; Deformation of the slope; entire distribution optimization; fuzzy time series; mine; ENROLLMENTS; INTERVALS; LENGTH;
D O I
10.1109/ACCESS.2020.3027206
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Because the deformation of the slope is affected by the stability of the underground structure, natural factors, and human factors, it is difficult for the traditional prediction model of the slope to accurately predict sudden changes. This paper proposes a method to predict the deformation of high and steep slopes based on the fuzzy time series and Entire Distribution Optimization. The division of the domain is optimized by the Entire Distribution Optimization, and the deformation of high and steep slopes is predicted by the fuzzy time series. The experimental results show that the fuzzy time series has a good predictive effect on the number of mutations, and the Entire Distribution Optimization avoids the one-sidedness of dividing the domain by mean, which improves the accuracy of the deformation forecasting model of the high and steep slope.
引用
收藏
页码:176112 / 176121
页数:10
相关论文
共 50 条
  • [41] A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches
    Singh, Pritpal
    Dhiman, Gaurav
    JOURNAL OF COMPUTATIONAL SCIENCE, 2018, 27 : 370 - 385
  • [42] A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks
    Aladag, Cagdas Hakan
    Yolcu, Ufuk
    Egrioglu, Erol
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2010, 81 (04) : 875 - 882
  • [43] Handling forecasting problems based on fuzzy time series model and model error learning
    Wu, Hao
    Long, Haiming
    Jiang, Jiancheng
    APPLIED SOFT COMPUTING, 2019, 78 : 109 - 118
  • [44] Deterministic fuzzy time series model for forecasting enrollments
    Li, Sheng-Tun
    Cheng, Yi-Chung
    COMPUTERS & MATHEMATICS WITH APPLICATIONS, 2007, 53 (12) : 1904 - 1920
  • [45] A refined fuzzy time-series model for forecasting
    Yu, HK
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2005, 346 (3-4) : 657 - 681
  • [46] AN ENHANCED DETERMINISTIC FUZZY TIME SERIES FORECASTING MODEL
    Li, Sheng-Tun
    Cheng, Yi-Chung
    CYBERNETICS AND SYSTEMS, 2009, 40 (03) : 211 - 235
  • [47] A different approach on fuzzy time series forecasting model
    Vamitha, V.
    MATERIALS TODAY-PROCEEDINGS, 2021, 37 : 125 - 128
  • [48] A Regime Switching Model for Fuzzy Time Series Forecasting
    Huarng, Kun-Huang
    Yu, Tiffany Hui-Kuang
    PROCEEDING OF THE 10TH INTERNATIONAL CONFERENCE ON INTELLIGENT TECHNOLOGIES, 2009, : 242 - 246
  • [49] Dynamic optimization of fuzzy cognitive maps for time series forecasting
    Salmeron, Jose L.
    Froelich, Wojciech
    KNOWLEDGE-BASED SYSTEMS, 2016, 105 : 29 - 37
  • [50] Surface Deformation Prediction Model of High and Steep Open-Pit Slope Based on APSO and TWSVM
    Du, Sunwen
    Song, Ruiting
    Qing, Qu
    Zhao, Zhiying
    Sun, Hailing
    Chen, Yanwei
    ELEKTRONIKA IR ELEKTROTECHNIKA, 2024, 30 (01) : 77 - 83