A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy

EasyChair Preprint 2347

Abstract

The entropy models have been recently adopted in many studies to evaluate the distribution of the shear stress in circular channels. However, the uncertainty in their predictions and their reliability remains an open question. We present a novel method to evaluate the uncertainty of four popular entropy models, including Shannon, Shannon-Power Low (PL), Tsallis, and Renyi, in shear stress estimation in circular channels. The Bayesian Monte-Carlo (BMC) uncertainty method is simplified considering a 95% Confidence Bound (CB). We developed a new statistic index called as FREE_opt-based OCB (FOCB) using the statistical indices Forecasting Range of Error Estimation (FREE) and the percentage of observed data in the CB (N_in), which integrates their combined effect. The Shannon and Shannon PL entropies had close values of the FOCB equal to 8.781 and 9.808, respectively, had the highest certainty in the calculation of shear stress values in circular channels followed by traditional uniform flow shear stress and Tsallis models with close values of 14.491 and 14.895, respectively. However, Renyi entropy with much higher values of FOCB equal to 57.726 has less certainty in the estimation of shear stress than other models. Using the presented results in this study, the amount of confidence in entropy methods in the calculation of shear stress to design and implement different types of open channels and their stability is determined.

Keyphrases: Entropy, Renyi, Shannon, Shannon PL, Tsallis, shear stress distribution, uncertainty

@booklet{EasyChair:2347,
  author    = {Amin Kazemian-Kale-Kale and Azadeh Gholami and Mohammad Rezaie-Balf and Amir Mosavi and Ahmed A Sattar and Bahram Gharabaghi and Hossein Bonakdari},
  title     = {A Bayesian Monte-Carlo Uncertainty Model for Assessment of Shear Stress Entropy},
  howpublished = {EasyChair Preprint 2347},
  year      = {EasyChair, 2020}}