Uncertainty analysis of shear stress estimation in circular channels by Tsallis entropy

被引:17
|
作者
Kazemian-Kale-Kale, Amin [1 ]
Bonakdari, Hossein [1 ]
Gholami, Azadeh [1 ]
Khozani, Zohreh Sheikh [1 ]
Akhtari, Ali Akbar [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; Shear stress; Box-Cox; Confidence bound; Circular channel; 2-DIMENSIONAL VELOCITY DISTRIBUTION; ONE-DIMENSIONAL VELOCITY; NEURAL-NETWORKS MODEL; FLOW DURATION CURVES; BOUNDARY SHEAR; INFORMATION-THEORY; MEAN VELOCITY; MOBILE BED; STABLE CHANNELS; BANK PROFILE;
D O I
10.1016/j.physa.2018.07.014
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Accurate prediction of the shear stress distribution is essential for the successful design of stable erodible-bed channels and for the sediment transport studies. Considerable attention in recent years has been given to the estimation of velocity distribution using entropy concept in open channels. Despite the importance of knowledge about shear stress distribution, there are very few studies on the application of the entropy methods for prediction of the shear stress distribution in open channels. The Tsallis entropy has been employed in this study for estimating the shear stress in open channels. In this approach, a pair of mean and maximum shear stresses are used to evaluate the shear stress distribution on the entire channel cross-section. We then calculated the prediction uncertainty of the shear stress obtained from the Tsallis entropy in a circular open channel. Moreover, the distribution of prediction error for the Tsallis approach is examined in two cases, both before and after data normalization. The quantitative results from this uncertainty analysis showed satisfactory results for the Tsallis entropy model for estimating shear stress in the entire section. The 95% Confidence Bounds (CB) are obtained for the shear stress distribution predicted by the model closely match the observed values. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:558 / 576
页数:19
相关论文
共 50 条
  • [31] Uncertainty analysis of flow velocity estimation by a simplified entropy model
    Corato, G.
    Melone, F.
    Moramarco, T.
    Singh, V. P.
    HYDROLOGICAL PROCESSES, 2014, 28 (03) : 581 - 590
  • [32] Apparent shear stress analysis in meandering compound channels
    Pradhan, Arpan
    Khatua, Kishanjit K.
    Dash, Saine S.
    RIVER FLOW 2016, 2016, : 472 - 479
  • [33] Boundary shear stress analysis in smooth rectangular channels
    Seckin, G
    Seckin, N
    Yurtal, R
    CANADIAN JOURNAL OF CIVIL ENGINEERING, 2006, 33 (03) : 336 - 342
  • [34] Shear stress analysis of grass-lined channels
    Panigrahi, B.
    Sharma, S.D.
    Senanati, P.C.
    Journal of the Institution of Engineers (India), Part AG: Agricultural Engineering Division, 1993, 74
  • [35] Linearized circular energy curve color image segmentation based on Tsallis entropy
    Wang, Shaoxun
    Fan, Jiulun
    Liu, Heng
    EXPERT SYSTEMS WITH APPLICATIONS, 2025, 273
  • [36] Tsallis entropy based uncertainty relations on sparse representation for vector and matrix signals
    Xu, Guanlei
    Xu, Xiaogang
    Wang, Xiaotong
    INFORMATION SCIENCES, 2022, 617 : 359 - 372
  • [37] Mathematical modelling of streamwise velocity profile in open channels using Tsallis entropy
    Kumbhakar, Manotosh
    Ray, Rajendra K.
    Chakraborty, Suvra Kanti
    Ghoshal, Koeli
    Singh, Vijay P.
    COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2021, 94
  • [38] Estimating the shear stress distribution in circular channels based on the randomized neural network technique
    Khozani, Zohreh Sheikh
    Bonakdari, Hossein
    Zaji, Amir Hossein
    APPLIED SOFT COMPUTING, 2017, 58 : 441 - 448
  • [39] One-Dimensional Velocity Distribution in Open Channels Using Tsallis Entropy
    Cui, Huijuan
    Singh, Vijay P.
    JOURNAL OF HYDROLOGIC ENGINEERING, 2014, 19 (02) : 290 - 298
  • [40] On the Estimation of Tsallis Entropy and a Novel Information Measure Based on Its Properties
    Marti, Aniol
    de Cabrera, Ferran
    Riba, Jaume
    IEEE SIGNAL PROCESSING LETTERS, 2023, 30 : 818 - 822