MANNING'S ROUGHNESS COEFFICIENT IN ALLUVIAL CHANNELS AFFECTED BY SEEPAGE

2005 ◽  
Vol 11 (3) ◽  
pp. 116-124
Author(s):  
Nagaraj Sitaram ◽  
Achanta Ramakrishna Rao
Keyword(s):  
Roughness Coefficient ◽  
Alluvial Channels ◽  
Manning’S Roughness Coefficient

scholarly journals Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

2017 ◽  
Vol 20 (2) ◽  
pp. 440-456
Author(s):  
J. Drisya ◽  
D. Sathish Kumar
Keyword(s):  
Genetic Algorithm ◽  
Flow Model ◽  
Overland Flow ◽  
Fitness Function ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
Automatic Data ◽  
Automated Calibration ◽  
Manning’S Roughness Coefficient ◽  
Overland Flow Model

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

scholarly journals Manning’s roughness coefficient for the Doce River

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Emmanuel Kennedy da Costa Teixeira ◽  
Márcia Maria Lara Pinto Coelho ◽  
Eber José de Andrade Pinto ◽  
Jéssica Guimarães Diniz ◽  
Aloysio Portugal Maia Saliba
Keyword(s):  
River Flow ◽  
Hydraulic Modeling ◽  
Roughness Coefficient ◽  
Local Approach ◽  
Water Courses ◽  
Time Variations ◽  
The Subject ◽  
Manning’S Roughness Coefficient ◽  
Doce River ◽  
Water Depths

ABSTRACT The Manning’s roughness coefficient is used for various hydraulic modeling. However, the decision on what value to adopt is a complex task, especially when dealing with natural water courses due to the various factors that affect this coefficient. For this reason, most of the studies carried out on the subject adopt a local approach, such as this proposal for the Doce River. Due to the regional importance of this river in Brazil, the objective of this article was to estimate the roughness coefficient of Manning along the river, in order to aid in hydraulic simulations, as well as to discuss the uncertainties and variations associated with this value. For this purpose, information on flow rates and water depths were collected at river flow stations along the river. With this information, the coefficients were calculated using the Manning equation, using the software Canal, and their space-time variations were observed. In addition, it was observed that the uncertainties in flow and depth measurements affect the value of the Manning coefficient in the case studied.

Prediction of form roughness coefficient in alluvial channels using efficient hybrid approaches

2020 ◽  
Vol 24 (24) ◽  
pp. 18531-18543
Author(s):  
Kiyoumars Roushangar ◽  
Seyed Mahdi Saghebian ◽  
V. S. Ozgur Kirca ◽  
Roghayeh Ghasempour
Keyword(s):  
Roughness Coefficient ◽  
Alluvial Channels ◽  
Hybrid Approaches

Manning's roughness coefficient for ecological subsurface channel with modules

2019 ◽  
Vol 18 (3) ◽  
pp. 349-361 ◽  
Author(s):  
Reza Mohammadpour ◽  
Muhammad Kashfy Zainalfikry ◽  
Nor Azazi Zakaria ◽  
Aminuddin Ab. Ghani ◽  
Ngai Weng Chan
Keyword(s):  
Roughness Coefficient ◽  
Manning’S Roughness Coefficient

scholarly journals Modeling total resistance and form resistance of movable bed channels via experimental data and a kernel-based approach

2020 ◽  
Vol 22 (3) ◽  
pp. 528-540
Author(s):  
Seyed Mahdi Saghebian ◽  
Kiyoumars Roushangar ◽  
V. S. Ozgur Kirca ◽  
Roghayeh Ghasempour
Keyword(s):  
Experimental Data ◽  
Reynolds Number ◽  
Laboratory Data ◽  
Gaussian Process Regression ◽  
Roughness Coefficient ◽  
Alluvial Channels ◽  
Sediment Characteristics ◽  
Modeling Process ◽  
The Impact ◽  
Movable Bed

Abstract An accurate prediction of roughness coefficient in alluvial channels is of substantial importance for river management. In this study, the total and form resistance in alluvial channels with dune bedform were assessed using experimental data. First, the data of experiments carried out at the Hydraulic Laboratory of University of Tabriz was used to investigate the impact of hydraulic and sediment parameters on roughness coefficient. Then, these data were combined with other laboratory data, and the total and bedform resistance were modeled via a Gaussian Process Regression (GPR) approach. For models, developing different input combinations were considered based on flow and sediment characteristics. The obtained results from the experiments showed that the Reynolds number has a better correlation with flow resistance in comparison with other hydraulic parameters. It was found that the roughness variations due to bedform are almost between 40 and 80% of the total roughness coefficient. Also, the obtained results proved the capability of the GPR method in the modeling process. It was found that the model which took the advantages of both flow and sediment characteristics performed better compared to the other models. The sensitivity analysis results showed that the Reynolds number has the most significant impact in the prediction process.

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.

Sign in / Sign up

Export Citation Format

Share Document