The uncertainty of the Shannon entropy model for shear stress distribution in circular channels

被引:8
|
作者
Kazemian-Kale-Kale, Amin [1 ]
Bonakdari, Hossein [1 ]
Gholami, Azadeh [1 ]
Gharabaghi, Bahram [2 ]
机构
[1] Razi Univ, Dept Civil Engn, Kermanshah, Iran
[2] Univ Guelph, Sch Engn, Guelph, ON N1G 2W1, Canada
关键词
Uncertainty analysis; Shear stress distribution; Shannon entropy; Confidence bound; Johnson transformation; Box-Cox transformation; Circular with sediment bed; PARTICLE SWARM OPTIMIZATION; BOUNDARY SHEAR; VELOCITY DISTRIBUTION; GENETIC ALGORITHM; BANK PROFILE; SMOOTH; DESIGN; GEOMETRY; MAXIMUM; PREDICT;
D O I
10.1016/j.ijsrc.2019.07.001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The shear stress distribution at alluvial stream beds and banks is one of the essential parameters in channel stability analysis. In the current paper, a novel uncertainty analysis method based on the framework of a Bayesian Forecasting System (BFS) is presented to evaluate the Shannon entropy model for prediction of the shear stress distribution in both circular rigid-bed and alluvial-bed channels. The Johnson and Box-Cox transformation functions were applied to select the optimum sample size (SS) and corresponding transformation factor for determining a 95% confidence bound (CB) for the Shannon entropy model. The Shapiro-Wilk (SW) test is applied according to the SS used to evaluate the power of transformation functions in the data normalization. The results show that the error distribution between predicted and experimental shear stress values generated using the Box-Cox transformation is closer to a Gaussian distribution than the generated using the Johnson transformation. The indexes of the percentage of the experimental values within the CB (N-in) and Forecast Range Error Estimate (FREE) are applied for the uncertainty analyses. The lower values of FREE equal to 1.724 in the circular rigid-bed channel represent the low uncertainty of Shannon entropy in the prediction of shear stress values compared to the uncertainty for the circular alluvial-bed channel with a FREE value equal to 7.647. (C) 2019 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:57 / 68
页数:12
相关论文
共 50 条
  • [41] Shear stress in open channels
    Novillo, MLO
    Pe, JA
    Méndez, AL
    INGENIERIA HIDRAULICA EN MEXICO, 2001, 16 (03): : 39 - 45
  • [42] Shannon Entropy in Uncertainty Quantification for the Physical Effective Parameter Computations of Some Nanofluids
    Kaminski, Marcin
    Ossowski, Rafal Leszek
    NANOMATERIALS, 2025, 15 (03)
  • [43] Shannon entropy, Fisher information and uncertainty relations for log-periodic oscillators
    Aguiar, V.
    Guedes, I.
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2015, 423 : 72 - 79
  • [44] Measure of Uncertainty in Process Models Using Stochastic Petri Nets and Shannon Entropy
    Ibl, Martin
    Capek, Jan
    ENTROPY, 2016, 18 (01)
  • [45] Shannon's entropy-based bank profile equation of threshold channels
    Cao, SY
    Knight, DW
    STOCHASTIC HYDRAULICS '96, 1996, : 169 - 175
  • [46] Simulation study of bed shear stress distribution of wide and narrow alternated channels
    Miao, Shu-Ting
    Lin, Hu
    Zeng, Yun
    CIVIL ENGINEERING AND URBAN PLANNING IV, 2016, : 529 - 532
  • [47] Sidewall shear stress distribution effects on cohesive bank erosion in curved channels
    Yu, Minghui
    Xie, Yaguang
    Wu, Songbai
    Tian, Haoyong
    PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-WATER MANAGEMENT, 2019, 172 (05) : 257 - 269
  • [48] Modeling Bed Shear Stress Distribution in Rectangular Channels Using the Entropic Parameter
    Mirauda, Domenica
    Russo, Maria Grazia
    ENTROPY, 2020, 22 (01) : 87
  • [49] Probability distribution and entropy as a measure of uncertainty
    Wang, Qiuping A.
    JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL, 2008, 41 (06)
  • [50] Generalized entropy and model uncertainty
    Meyer-Gohde, Alexander
    JOURNAL OF ECONOMIC THEORY, 2019, 183 : 312 - 343