TY - JOUR
A1 - Lashkar-Ara, Babak
A1 - Kalantari, Niloofar
A1 - Sheikh Khozani, Zohreh
A1 - Mosavi, Amir
T1 - Assessing Machine Learning versus a Mathematical Model to Estimate the Transverse Shear Stress Distribution in a Rectangular Channel
T2 - Mathematics
N2 - One of the most important subjects of hydraulic engineering is the reliable estimation of the transverse distribution in the rectangular channel of bed and wall shear stresses. This study makes use of the Tsallis entropy, genetic programming (GP) and adaptive neuro-fuzzy inference system (ANFIS) methods to assess the shear stress distribution (SSD) in the rectangular channel.
To evaluate the results of the Tsallis entropy, GP and ANFIS models, laboratory observations were used in which shear stress was measured using an optimized Preston tube. This is then used to measure the SSD in various aspect ratios in the rectangular channel. To investigate the shear stress percentage, 10 data series with a total of 112 different data for were used. The results of the sensitivity analysis show that the most influential parameter for the SSD in smooth rectangular channel is the dimensionless parameter B/H, Where the transverse coordinate is B, and the flow depth is H. With the parameters (b/B), (B/H) for the bed and (z/H), (B/H) for the wall as inputs, the modeling of the GP was better than the other one. Based on the analysis, it can be concluded that the use of GP and ANFIS algorithms is more effective in estimating shear stress in smooth rectangular channels than the Tsallis entropy-based equations.
KW - Maschinelles Lernen
KW - smooth rectangular channel
KW - Tsallis entropy
KW - genetic programming
KW - artificial intelligence
KW - machine learning
KW - big data
KW - computational hydraulics
Y1 - 2021
UR - https://e-pub.uni-weimar.de/opus4/frontdoor/index/index/docId/4419
UR - https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20210504-44197
UR - https://www.mdpi.com/2227-7390/9/6/596
VL - 2021
IS - Volume 9, Issue 6, Article 596
PB - MDPI
CY - Basel
ER -