Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels
Abstract Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis.
Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels
Abstract Use of genetic programming (GP) in the field of river engineering is rare. During flood when the water overflows beyond its main course known as floodplain encounters various obstacles through rough materials and vegetation. Again the flow behaviour becomes more complex in a compound channel section due to shear at different regions. Discharge results from the experimental channels for varying roughness surfaces, along with data from a compound river section, are used in the GP. Model equations are derived for prediction of discharge in the compound channel using five hydraulic parameters. Derived models are tested and compared with other soft computing techniques. Few performance parameters are used to evaluate all the approaches taken for analysis. From the sensitivity analysis, the effects of parameters responsible for the flow behaviour are inferred. GP is found to give the most potential results with the highest level of accuracy. This work aims to benefit the researchers studying machine learning approaches for application in stream flow analysis.
Genetic Programming: A Complementary Approach for Discharge Modelling in Smooth and Rough Compound Channels
Adhikari, Alok (Autor:in) / Adhikari, N. (Autor:in) / Patra, K. C. (Autor:in)
Journal of The Institution of Engineers (India): Series A ; 100 ; 395-405
07.02.2019
11 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
FIS , ANFIS , GP Engineering , Civil Engineering
Discharge Ratios in Smooth and Rough Compound Channels
| Online Contents | 1997
Discharge Ratios in Smooth and Rough Compound Channels
| British Library Online Contents | 1997
Boundary Shear in Smooth and Rough Channels
| ASCE | 2021
Stage-Discharge Prediction for Straight and Smooth Compound Channels with Wide Floodplains
| British Library Online Contents | 2012
Stage-Discharge Prediction for Straight and Smooth Compound Channels with Wide Floodplains
| Online Contents | 2012