Gene expression programming to predict Manning’s n in meandering flows

2018 ◽  
Vol 45 (4) ◽  
pp. 304-313 ◽  
Author(s):  
Arpan Pradhan ◽  
Kishanjit K. Khatua
Keyword(s):  
Gene Expression ◽  
Field Study ◽  
Discharge Capacity ◽  
Gene Expression Programming ◽  
Roughness Coefficient ◽  
Compound Channel ◽  
Factors Affecting ◽  
Manning’S N ◽  
Bed Material ◽  
Manning's N

Accurate prediction of Manning’s roughness coefficient is essential for the computation of conveyance capacity in open channels. There are various factors affecting the roughness coefficient in a meandering compound channel and not just the bed material. The factors, geometric as well as hydraulic, are investigated and incorporated in the prediction of Manning’s n. In this study, a new and accurate technique, gene expression programming (GEP) is used to estimate Manning’s n. The estimated value of Manning’s n is used in the evaluation of the conveyance capacity of meandering compound channels. Existing methods on conveyance estimation are assessed to carry out a comparison between them and the proposed GEP model. Results show that the discharge capacity computed by the new model provides far better results than the traditional models. The developed GEP model is validated with three individual sections of a natural river, signifying that the model can be applied to field study of rivers, within the stated range of parameters.

Manning’s N Value of Bridge-in-Backpack

2014 ◽  
Vol 638-640 ◽  
pp. 965-968
Author(s):  
Jing Ma ◽  
Ling Qiang Yang
Keyword(s):  
Water Flow ◽  
Roughness Coefficient ◽  
N Value ◽  
New Type ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

Bridge-in-a-Backpack is a new type bridge. this study will investigate the interaction of flow under the bridge with the tubes and decking, and recommend Manning’s roughness coefficient for water flow under the composite backbridge system.

scholarly journals Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter

2018 ◽  
Vol 10 (10) ◽  
pp. 1505 ◽  
Author(s):  
Yuval Sadeh ◽  
Hai Cohen ◽  
Shimrit Maman ◽  
Dan Blumberg
Keyword(s):  
Surface Roughness ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Roughness Coefficient ◽  
Arid Environments ◽  
Hydrological Models ◽  
Radar Backscatter ◽  
Manning’S Roughness Coefficient ◽  
Manning’S N ◽  
Manning's N

The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

Gene expression programming for total bed material load estimation—a case study

2010 ◽  
Vol 408 (21) ◽  
pp. 5078-5085 ◽  
Author(s):  
Nor Azazi Zakaria ◽  
H. Md. Azamathulla ◽  
Chun Kiat Chang ◽  
Aminuddin Ab. Ghani
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Load Estimation ◽  
Material Load ◽  
Bed Material

scholarly journals The Manning’s Roughness Coefficient Calibration Method to Improve Flood Hazard Analysis in the Absence of River Bathymetric Data: Application to the Urban Historical Zamora City Centre in Spain

2021 ◽  
Vol 11 (19) ◽  
pp. 9267
Author(s):  
Julio Garrote ◽  
Miguel González-Jiménez ◽  
Carolina Guardiola-Albert ◽  
Andrés Díez-Herrero
Keyword(s):  
Urban Areas ◽  
Flood Hazard ◽  
Flood Damage ◽  
City Centre ◽  
Average Error ◽  
Accurate Estimation ◽  
Roughness Coefficient ◽  
Flow Depth ◽  
Manning’S N ◽  
Manning's N

The accurate estimation of flood risk depends on, among other factors, a correct delineation of the floodable area and its associated hydrodynamic parameters. This characterization becomes fundamental in the flood hazard analyses that are carried out in urban areas. To achieve this objective, it is necessary to have a correct characterization of the topography, both inside the riverbed (bathymetry) and outside it. Outside the riverbed, the LiDAR data led to an important improvement, but not so inside the riverbed. To overcome these deficiencies, different models with simplified bathymetry or modified inflow hydrographs were used. Here, we present a model that is based upon the calibration of the Manning’s n value inside the riverbed. The use of abnormally low Manning’s n values made it possible to reproduce both the extent of the flooded area and the flow depth value within it (outside the riverbed) in an acceptable manner. The reduction in the average error in the flow depth value from 50–75 cm (models without bathymetry and “natural” Manning’s n values) to only about 10 cm (models without bathymetry and “calibrated” Manning’s n values), was propagated towards a reduction in the estimation of direct flood damage, which fell from 25–30% to about 5%.

Sign in / Sign up

Export Citation Format

Share Document