Stage discharge prediction in heterogeneous compound open channel roughness

Parsaie, Abbas; Najafian, Sharareh; Omid, M H; Yonesi, Hojjatallah

doi:10.1080/09715010.2016.1235471

Cited by 15 publications

(4 citation statements)

References 60 publications

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“…Observing the relation between these variables, for each cross section of each station, it was noticed that when the section tends to have two defined beds, one bigger and one smaller, the relation between the variables tends to be directly proportional when the flow occurs in the higher bed and inversely proportional when the flow is at the lowest, as expected. This happens because the larger bed may be the floodplain, where there are several materials, such as vegetation, gravel, among others, which increases the value of Manning (PARSAIE et al, 2017). While in the lower bed, where consequently the flow has a lower depth, the effects of canal bottom irregularities are more evident, which increases Manning's value (CHOW, 1959).…”

Section: Relation Between Water Depth and Manning Coefficientmentioning

confidence: 99%

Manning’s roughness coefficient for the Doce River

Teixeira

Coelho

Pinto

et al. 2018

RBRH

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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. RESUMOO coeficiente de rugosidade Manning é utilizado em várias modelagens hidráulicas. Entretanto, a decisão sobre qual valor será adotado é uma tarefa complexa, principalmente se tratando de cursos d'água naturais, devido aos vários fatores que afetam esse coeficiente. Por isso, na maioria dos estudos realizados sobre o tema se adota uma abordagem local, como a proposta para o Rio Doce. Devido à importância regional deste rio no Brasil, o objetivo deste artigo foi estimar a rugosidade de Manning ao longo do Rio Doce, de forma a auxiliar em simulações hidráulicas, e também discutir as incertezas e as variações associadas a esse valor. Para isso, foram levantadas informações de vazões e profundidades da água em estações fluviométricas ao longo do rio. De posse dessas informações, calcularam-se os referidos coeficientes utilizando-se a equação de Manning, sendo que para isso utilizou-se o software Canal, e foram observadas suas variações espaço-temporal. Além disso, foi observado como as incertezas nas medições de vazões e de profundidades afetam o valor do coeficiente de Manning no caso estudado. Palavras-chave:Incertezas no coeficiente de rugosidade; Modelagem hidráulica; Simulação de Monte Carlo.RBRH, Porto Alegre, v. 23, e52, 2018Manning's roughness coefficient for the Doce River 2/12

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Section: Relation Between Water Depth and Manning Coefficientmentioning

confidence: 99%

Manning’s roughness coefficient for the Doce River

Teixeira

Coelho

Pinto

et al. 2018

RBRH

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“…These very basic equations have inherent importance, but they are also significant because they support more sophisticated model is used to simulate progressively changing stable and unstable flows, where they represent the frictional gradient that permits for restriction to flow through streams and rivers. Manning's condition is quite possibly the most usually utilized conditions for deciding the pace of stream in an open channel with uniform stream [1]. This equation expresses the equilibrium between the prime motivator (gravity) and the flow resistance, which is represented by Manning's roughness coefficient 'n'.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between Manning Roughness Coefficient and Flow Depth in Bangladesh Rivers

Tahmid

Rahman

Mounota

et al. 2021

MJCE

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Manning's n is the most widely used resistance coefficient for open channel flows. There are several factors that affecting the variation of roughness coefficient in open channels such as surface roughness, bed material, channel alignment, shape irregularity and vegetation. The prediction of the variation of the roughness coefficient in a natural waterway becomes more complex and challenging task to hydraulics engineers until now. The main emphasis of this research is the assessment of the Manning coefficient of riverside roughness, which is used in hydraulic simulations and to explore the link between the coefficient of Manning and water depth The aim of this study was to investigate the correlation/relationship between flow depth and Manning's n for several selected rivers in Bangladesh. This research represents graphically the connection between roughness coefficient of Manning and water depth of year 2019 based on the collected data's (cross section, discharge, stream width) from Bangladesh Water Development Board (BWDB). The main focus of this research was to establish the regression equations by graphically plotting calculated Manning's n versus flow depth. The relationship between the two variables in the stations is shown to be directly proportional, while some are inversely proportional, by changing water depth and computing Manning roughness coefficient. It can be seen that most stations have more than one behavior, i.e., the connection between these parameters is directly related in certain periods all through the year, while it is inversely proportional with others. The findings prevail that the Manning's n varied from 0.01 s/m1/3 to 0.14 s/m1/3 for a comparable depth of 1m to 20m at all the stations being studied here and 6th order polynomial equation observed R2 is between 0.9288 and 0.9943 for most of the stations being studied here which may provide an efficient prediction evaluation in estimating Manning roughness coefficient.

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“…Various researchers have been used various data mining and data-driven techniques in civil and environmental engineering applications (Mehdipour, Stevenson, Memarianfard, & Sihag, 2018;Nain, Sihag, & Parsaie, 2016;Parsaie, Azamathulla, & Haghiabi, 2018;Parsaie & Haghiabi, 2017, 2015Parsaie, Najafian, Omid, & Yonesi, 2017;Sihag, 2018;Sihag, Singh, Vand, & Mehdipour, 2018a;Sihag, Tiwari, & Ranjan, 2018bSingh, Sihag, & Singh, 2017;Singh, Sihag, Singh, & Kumar, 2018;Tiwari, Sihag, Kumar, & Ranjan, 2018;Vand, Sihag, Singh, & Zand, 2018). Asadollahfardi, Hemati, Moradinejad, and Asadollahfardi (2013) used ANN to predict the SAR in which Na, Mg, Ca, SO 4 , Cl, HCO − 3 , pH were input variables and SAR was output and found that ANN has the capability to predict the SAR accurately and precisely.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the sodium absorption ratio using data-driven models: a case study in Iran

Singh

2019

Geology, Ecology, and Landscapes

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In this investigation, two data-driven models, i.e., Gaussian Process (GP) and Support Vector Machine (SVM), were used to predict the sodium absorption ratio (SAR) in three subwatersheds (Khorramabad, Biranshahr, and Alashtar) in Iran. A comparison was also done with these data-driven models with Artificial Neural Network (ANN). The parameters total dissolved solids, electrical conductivity, pH value, CO 3 , HCO 3 , chlorine (Cl), SO 4 , calcium (Ca), magnesium (Mg), sodium (Na), and potassium (K) were used as input variables and SAR as output. For SVM and GP regression, two kernel functions (radial-based kernel and Person VII kernel function) were used. The results from this investigation suggest that the ANN model (correlation coefficient [CC], root mean square error [RMSE], Nash-Sutcliffe coefficient of efficiency [NSC], and mean absolute relative error [MARE] = 0.9966, 0.0286, 0.9906, and 0.0194) is more precise as compared to the GP (CC, RMSE, NSC, and MARE = 0.9570, 0.2982, 0.8288, and 0.3705) and SVM (CC, RMSE, NSC, and MARE = 0.9948, 0.0365, 0.9847, and 0.063). Among GP and SVM, SVM with PUK kernel is more accurate for estimating the SAR of the watershed. Thus, ANN is a technique which could be used for predicting the SAR for given study area. ARTICLE HISTORY

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Stage discharge prediction in heterogeneous compound open channel roughness

Cited by 15 publications

References 60 publications

Manning’s roughness coefficient for the Doce River

Manning’s roughness coefficient for the Doce River

Relationship Between Manning Roughness Coefficient and Flow Depth in Bangladesh Rivers

Prediction of the sodium absorption ratio using data-driven models: a case study in Iran

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