Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods

Toprak, Z. Fuat; Hamidi, Nizamettin; Kisi, Özgür; Gerger, Reşit

doi:10.1007/s12205-014-0089-y

Cited by 21 publications

(14 citation statements)

References 80 publications

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“…For obtaining D 1 there are various procedures available in the current literature [4]. As can be easily understood from Taylor (1953) and Taylor (1954) and are now continued by many other researchers who have tried to explain the longitudinal dispersion mechanism in prismatic and natural channels [4][5][6][7][8][9][10][11][12][13][14].…”

Section: The Physical Mechanism Of the Longitudinal Dispersionmentioning

confidence: 99%

“…Nonetheless, they may still be considered a basis for the understanding of the physical mechanism of dispersion. Fischer's study, in particular, was a major contribution in moving the studies from their infancy into a more developed stage, which includes studies by Yotsukura et al (1970), Atesman (1970), Godfrey and Frederick (1970), Bansal (1970), Sümer (1970), Bansal (1971) [12]. These studies are more confident, the measurements and/or data are more accurate, and there is a significant development in computer technology.…”

Section: Background Of the Literature Of Ldcmentioning

confidence: 99%

“…Although there is a large number of published studies that propose models to predict dimensional LDC, there are only a few published works that propose models to estimate dimensionless LDC. However, the equations using dimensionless LDC (D 1 ') have more generality than the dimensional ones [12]. In almost all modeling techniques the media has been assumed as one-dimensional.…”

Section: Focusing On the Assumptions Made In Modeling Ldcmentioning

confidence: 99%

“…In other words, Equation (1) is not valid at the initial times and around the entry place of the pollutant into the channel. Toprak and Savci (2007), Toprak and Cigizoglu (2008) [9][10], and Toprak et al (2014) [12] indicate that for validation of Equation (1), there must be a sufficient time duration and distance from the source so that the pollutant is uniformly mixed with the water.…”

Section: XIIImentioning

confidence: 99%

“…Elder (1959) [17], Fischer (1975) [40], Liu (1977) [42], Seo and Cheong (1998) [56], Koussis and Rodriguez-Mirasol (1998) [55], Kashefipour and Falconer (2002) [66], [4], Toprak and Savci (2007), Toprak and Cigizoglu (2008) [9][10], and Toprak et al (2014) [12] use the same variables for modeling. So do the works that are reviewed in detail here.…”

Section: XIIImentioning

confidence: 99%

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A Review of Proposed Techniques for Modeling Longitudinal Dispersion Coefficient in Natural Channels

Toprak

2016

JWRHE

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Abstract-The World is facing a global climate change, while the world's population continues to grow. Therefore, providing sufficient water has become increasingly difficult in the last few decades. Artificially providing fresh water requires complex technology and economic investment. This is almost impossible, especially in underdeveloped countries. Due to these issues, water scarcity and/or stress is expected to rise dramatically in many regions in the world in the near future. Furthermore, current fresh water resources are vulnerable to pollutants (nuclear, biological, physical, and chemical). Conservation of fresh water resources has therefore become a critical issue. As is well known, the main fresh water sources are rivers and natural streams. Convective longitudinal dispersion is the most effective parameter in such sources. The longitudinal dispersion coefficient represents the rate of pollution and it is mostly used in water pollution modeling studies. Hence, learning the longitudinal dispersion mechanism in natural channels is vitally important to control water pollution. This paper's main purpose is to present a significant review and criticism on the literature related to both the longitudinal dispersion mechanism and the modeling techniques proposed for determining longitudinal dispersion coefficient in natural channels.

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Section: The Physical Mechanism Of the Longitudinal Dispersionmentioning

confidence: 99%

Section: Background Of the Literature Of Ldcmentioning

confidence: 99%

Section: Focusing On the Assumptions Made In Modeling Ldcmentioning

confidence: 99%

Section: XIIImentioning

confidence: 99%

Section: XIIImentioning

confidence: 99%

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A Review of Proposed Techniques for Modeling Longitudinal Dispersion Coefficient in Natural Channels

Toprak

2016

JWRHE

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Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient

Alizadeh

Ahmadyar

Afghantoloee

2017

Water Resour Manage

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Accurate prediction of longitudinal dispersion coefficient (K) is a key element in studying of pollutant transport in rivers when the full cross sectional mixing has occurred. In this regard, several research studies have been carried out and different equations have been proposed. The predicted values of K obtained by different equations showed a great amount of uncertainty due to the complexity of the phenomenon. Therefore, there is still a need to make an improvement on the existing predictive models. In this study, a multi-objective particle swarm optimization (PSO) technique was used to derive new equations for predicting longitudinal dispersion coefficient in natural rivers. To do this, extensive field data, including hydraulic and geometrical characteristics of different rivers were applied. The results of this study were compared with those of the previous studies using the statistical error measures. The comparison revealed that the proposed model is superior to the previous ones. According to this study, PSO algorithm can be applied to improve the performance of the predictive equations by finding optimum values of the coefficients. The proposed model can be successfully applied to estimate the longitudinal dispersion coefficient for a wide range of rivers' characteristics.

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Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers

Ghaemi

Zhian

Pirzadeh

et al. 2021

Environ Sci Pollut Res

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The longitudinal dispersion coefficient (LDC) of river pollutants is considered as one of the prominent water quality parameters. In this regard, numerous research studies have been conducted in recent years, and various equations have been extracted based on hydrodynamic and geometric elements. LDC’s estimated values obtained using different equations reveal a significant uncertainty due to this phenomenon’s complexity. In the present study, the crow search algorithm (CSA) is applied to increase the equation’s precision by employing evolutionary polynomial regression (EPR) to model an extensive amount of geometrical and hydraulic data. The results indicate that the CSA improves the performance of EPR in terms of R2 (0.8), Willmott’s index of agreement (0.93), Nash–Sutcliffe efficiency (0.77), and overall index (0.84). In addition, the reliability analysis of the proposed equation (i.e., CSA) reduced the failure probability (Pf) when the value of the failure state containing 50 to 600 m2/s is increasing for the Pf determination using the Monte Carlo simulation. The best-fitted function for correct failure probability prediction was the power with R2 = 0.98 compared with linear and exponential functions.

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Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods

Cited by 21 publications

References 80 publications

A Review of Proposed Techniques for Modeling Longitudinal Dispersion Coefficient in Natural Channels

A Review of Proposed Techniques for Modeling Longitudinal Dispersion Coefficient in Natural Channels

Improvement on the Existing Equations for Predicting Longitudinal Dispersion Coefficient

Reliability-based design and implementation of crow search algorithm for longitudinal dispersion coefficient estimation in rivers

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