Identification of Manning’s Roughness Coefficients in Shallow Water Flows

Ding, Yan; Jia, Yafei; Wang, Sam S. Y.

doi:10.1061/(asce)0733-9429(2004)130:6(501)

Cited by 110 publications

(64 citation statements)

References 36 publications

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“…Other parameters usually require extra in-situ observations to accurately calculate the river slope, or the roughness coefficient that depends on the bed material, and often needs to be calculated empirically. [14] Previous studies used idealized conditions in hydraulic laboratories -constrained by the capacity and flow design limitations from their water tanks/channels, or modeling techniques (e.g., [13,14,46] ). Here, our maximum river discharge is similar to the average discharge of the Yangtze River in China, and we include the usual range of n and S values.…”

Section: Datamentioning

confidence: 99%

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Can we obtain viable alternatives to Manning’s equation using genetic programming?

Gaitán

Balaji

Moore

2016

AIR

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Applied water research, like the one derived from open-channel hydraulics, traditionally links empirical formulas to observational data; for example Manning's formula for open channel flow driven by gravity relates the discharge (Q), cross-sectional average velocity (V ), the hydraulic radius (R), and the slope of the water surface (S) with a friction coefficient n, characteristic of the channel's surface needed in the location of interest. Here we use Genetic Programming (GP), a machine learning technique inspired by nature's evolutionary rules, to derive empirical relationships based on synthetic datasets of the aforementioned parameters. Specifically, we evaluated if Manning's formula could be retrieved from datasets with: a) 300 pentads of A, n, R, S, and Q (from Manning's equation), b) from datasets containing an uncorrelated variable and the parameters from (a), and c) from a dataset containing the parameters from (b) but using values of Q containing noise. The cross-validated results show success retrieving the functional form from the synthetic data in the first two experiments, and a more complex solution of Q for the third experiment. The results encourage the application of GP on problems where traditional empirical relationships show high biases or are non-parsimonious. The results also show alternative flow equations that might be used in the absence of one or more predictors; however, these equations should be used with caution outside of the training intervals.

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Section: Datamentioning

confidence: 99%

“…Ayvaz [13] and Ding, Jia [14] ) and/or for different riverbed materials (e.g. Candela, Noto [15] ), as even the presence of biological soil crusts can affect the surface roughness, runoff and erodibility of the channel.…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

Can we obtain viable alternatives to Manning’s equation using genetic programming?

Gaitán

Balaji

Moore

2016

AIR

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“…They rely on three main elements: an objective function that measures the discrepancy between observations and numerical results, an optimisation algorithm that adjusts parameters to reduce the value of the function, and a convergence criterion that tests its current value. Such a parameter values fitting method has been widely used for research purposes in river hydraulics over the last 30 years (see for example Anastasiadou-Partheniou and Samuels, 1998;Ding et al, 2004). The major drawback of single-objective optimisation stands in the equifinality problem which predicts that the same result might be achieved by different parameter sets (Beven and Binley, 1992;Spear, 1997).…”

Section: Parameter Values Fittingmentioning

confidence: 99%

River model calibration, from guidelines to operational support tools

Vidal¹,

Moisan²,

Faure³

et al. 2007

Environmental Modelling & Software

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Manuscript AbstractNumerical modelling is now used routinely to make predictions about the behaviour of environmental systems. Model calibration remains a critical step in the modelling process and different approaches have been taken to develop guidelines to support engineers and scientists in this task. This article reviews currently available guidelines for a river hydraulics modeller by dividing them into three types: on the calibration process, on hydraulic parameters, and on the use of hydraulic simulation codes. The article then presents an integration of selected guidelines within a knowledge-based calibration support system. A prototype called CaRMA-1 (Calibration of River Model Assistant) has been developed for supporting the calibration of models based on a specific 1D code. Two case studies illustrate the ability of the prototype to face operational situations in river hydraulics engineering, for which both data quality and quantity are not sufficient for an optimal calibration. Using CaRMA-1 allows the modeller to achieve the calibration task in accordance with good calibration practice implemented in the knowledge base. Relevant reasoning rules can easily be added to the knowledge base to extend the prototype range of applications. This study thus provides a framework for building operational support tools from various types of existing engineering guidelines.

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“…In order to reduce prediction errors or uncertainties, field measurements are usually used to verify and calibrate a model before applying it to make predictions. Traditional trial and error approaches are commonly used in model calibration, but they are known for being subjective and tedious (Ding, 2004). Therefore, in order to make a better prediction, it would be more beneficial to have more intelligent calibration methods achieved by fusing a dynamic flood model with observed information to obtain an optimal estimate of model states and parameters.…”

Section: Introductionmentioning

confidence: 99%

“…It has been widely applied to assimilate in situ and remotely sensed hydrological data from multi-sources into the runoff-rainfall model and land surface model (Bateni et al, 2013;Le Dimet et al, 2009;Lee et al, 2012;Reichle, 2008). Also, it has been successfully applied to improve the predictive capability of 1-D and 2-D hydraulic models (Atanov et al, 1999;Bélanger and Vincent, 2005;Ding, 2004;Honnorat et al, 2007Honnorat et al, , 2009; Roux and Dartus, 2006).…”

Section: Introductionmentioning

confidence: 99%

Variational assimilation of remotely sensed flood extents using a 2-D flood model

Lai

Liang

Daillet

2014

Hydrol. Earth Syst. Sci.

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Abstract.A variational data assimilation (4D-Var) method is proposed to directly assimilate flood extents into a 2-D dynamic flood model to explore a novel way of utilizing the rich source of remotely sensed data available from satellite imagery for better analyzing or predicting flood routing processes. For this purpose, a new cost function is specially defined to effectively fuse the hydraulic information that is implicitly indicated in flood extents. The potential of using remotely sensed flood extents for improving the analysis of flood routing processes is demonstrated by applying the present new data assimilation approach to both idealized and realistic numerical experiments.

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Identification of Manning’s Roughness Coefficients in Shallow Water Flows

Cited by 110 publications

References 36 publications

Can we obtain viable alternatives to Manning’s equation using genetic programming?

Can we obtain viable alternatives to Manning’s equation using genetic programming?

River model calibration, from guidelines to operational support tools

Variational assimilation of remotely sensed flood extents using a 2-D flood model

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