A simplified method for estimating the longitudinal dispersion coefficient in ecological channels with vegetation

Huai, Wenxin; Shi, Haoran; Song, S.L.; Ni, Shaoqiang

doi:10.1016/j.ecolind.2017.05.015

Cited by 29 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The study of pollutant and contaminant transport in the natural streams is important in several aspects such as quality management, pollution control of the chemical and biological processes, hydro-ecological studies and environmental impact assessments [1,2]. Hazardous pollutants and effluents, when discharged to the streams, in the mixing process, dispersed transversely, vertically, and longitudinally by advective and dispersive processes.…”

Section: Introductionmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

View full text Add to dashboard Cite

In this paper, a new hybrid model is developed to improve the accuracy in the prediction of the longitudinal dispersion coefficient (Kx) and the derivation of novel optimized explicit equations for natural streams. For this purpose, an artificial neural network (ANN) is hybridized with particle swarm optimization (PSO) and cat swarm optimization (CSO) algorithms. The CSO and PSO are used to find the optimum values of biases and weights in ANN structure and formulate the results as novel explicit predictive equations than the classical black-box methods. The hydraulic parameters of the natural stream and some geometric parameters were utilized for the model developments. Eight different input combinations are used as the input vectors to ANN, ANN-PSO, and ANN-CSO models, whereas the dispersion coefficient (Kx) is the target model output. The developed models are trained and tested bya comprehensive reference data sets measured on streams in the United States, that were used previously by Tayfur and Singh (2005) in ANN models. The main aims, novelty, and contributions of the present study are 1) improving the accuracy of classical ANN-based Kx predictions by hybridizing with CSO and PSO. 2) Performing sensitive analysis of ANN, ANN-CSO, and ANN-PSO based on input combinations 3) derivation of novel explicit optimized ANN-CSO, ANN-PSO, equations for predicting Kx rather than the classical ANN black-box methods. The results depicted that the highest accuracy and superiority were attained by the ANN-PSO model, with input variables of B, H, U, U*, followed by ANN-CSO and ANN. By using the optimized trained black box ANN models, two novel explicit predictive equations are derived, and their results are compared with the empirical equations. Comparative assessments confirmed significant improvements in the hybrid equations' results than the classical ANN and previously published equations. The developed novel equations can be used to estimate the Kx in one-dimensional pollutant transfer models that are essential for the pollution studies in environmental river engineering practices.

show abstract

Section: Introductionmentioning

confidence: 99%

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Consequently, estimation of the longitudinal dispersion coefficient could be useful in the management of water quality in rivers and optimal pollution control strategies [19], [20]. Seo and Cheong [11] stated that the K x is affected by three groups of factors, including hydraulic river features, vegetation, fluid properties and geometric patterns of river reach [21], [22]. The longitudinal dispersion coefficient measurements showed that the most effective parameters are channel width (B), flow depth (H ), bed shear velocity (U * ) and cross-sectional average flow velocity (U ) [23].…”

Section: Introductionmentioning

confidence: 99%

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

et al. 2020

View full text Add to dashboard Cite

In this paper, a new hybrid model is proposed using Subset Selection by Maximum Dissimilarity (SSMD) and adaptive neuro-fuzzy inference system (ANFIS) hybridized with the firefly algorithm (FFA) to predict the longitudinal dispersion coefficient (K x). The proposed framework (ANFIS-FFA), combines the specific structures and strengths of both ANFIS and FFA approaches. The FFA is used to derive the optimum ANFIS parameters. The K x data set includes 503 cross-sectional data point from small to large rivers. For pre-processing of the data set, the SSMD method is used, which is superior to the classical trial and error method. The database covers a wide range of river width (0.2-867m), and depths (0.034-19.9 m). Fifteen different combinations of river width (B), depth (H), flow velocity (U) and shear velocity (U *) are implemented as inputs to create fifteen estimative models. The output of the ANFIS-FFA model is compared with the ANFIS and previously published equations to check the performance of the proposed model. The results show that the highest accuracy is attained by the M1 model, with all geometric and hydrodynamic parameters as input variables in comparison with ANFIS and previous equations. The R 2 value, RMSE, MAE and NSE for ANFIS-FFA model are 0.67, 113.14 m 2 /s, 48 m 2 /s, and 0.63 for proposed dimensional model, and 0.35, 874.5, 520.8, and 0.1 in non-dimensional ANFIS-FFA model, respectively. These values were 0.37, 463.34 m 2 /s, 85.69 m 2 /s, and −5.19 for dimensional ANFIS model, and 0.11, 3269.88, 1932.09 and −11.54 for non-dimensional ANFIS model, respectively. Overall, hybridization caused 81%, 75%, 76% improvement in R 2 , RMSE and MAE. In another contribution of the paper, by using the matrix form of developed ANFIS-FFA optimized parameters, a novel explicit calculation procedure for estimation of K x is derived. Based on the results, the proposed ANFIS-FFA model exhibits significant improvements than the classical ANFIS and highlights that optimizing by nature-inspired optimization algorithms plays a critical role in strengthening the ANFIS estimations generality. INDEX TERMS Longitudinal dispersion coefficient, ANFIS-FFA, maximum dissimilarity method, natural rivers, adaptive neuro-fuzzy inference system.

show abstract

“…(a) Technical limitations and high cost (material, manpower, and time) in characterizing in detail real‐world aquifer (or stream) flow conditions and the evolutions of the condition. For example, the ideal pre‐estimation method of D in a field application has not been well established, although some preliminary tests have been done (Huai, Shi, Song, & Ni, 2018; Nofuentes & Polo, 2012; Shen, Niu, Anderson, & Phanikumar, 2010; Wang & Huai, 2016). (b) The explicit relationship between flow field characteristics and parameters of FADEs is unclear.…”

Section: Challenges and Suggestions For Future Workmentioning

confidence: 99%

A review of applications of fractional advection–dispersion equations for anomalous solute transport in surface and subsurface water

Sun

Qiu

et al. 2020

WIREs Water

View full text Add to dashboard Cite

Fractional advection–dispersion equations (FADEs) have been widely used in hydrological research to simulate the anomalous solute transport in surface and subsurface water. However, a large gap still exists between real‐world application (i.e., being a prediction tool) and theoretical FADEs. To better understand this disparity, the FADEs are firstly reviewed from the perspective of fractional‐in‐time and fractional‐in‐space, as well as the anomalous characteristics described by those functions. Then, challenges for the application of FADEs are summarized, including the theoretical gap of FADEs that needs multidisciplinary efforts to fill, extensive requirements for computation techniques and mathematical knowledge to apply the FADEs, the poor predictability for most parameters in the FADEs, and the limitations for collecting geologic information of flow fields. Then, some suggestions are given for future work, such as developing excellent code sets and mature simulation software with a friendly interface. This kind of work would alleviate the computation workload of hydrologists, especially those without coding expertise. Summarizing and determining the value ranges of the important parameters are needed (e.g., the order of the fractional derivative), through the extensive field, laboratory, and numerical experiments rather than blindly using their mathematical ranges. It is also needed to perform global sensitivity analysis for the FADEs. Meanwhile, comprehensive comparison work is necessary for suggesting a model suitable for a specific problem. Last but not least, further research is clearly needed to establish a link between nonlocal parameters and the heterogeneity property to develop more efficient fractional order partial differential equations. This article is categorized under: Science of Water > Methods Science of Water > Water Quality

show abstract

A simplified method for estimating the longitudinal dispersion coefficient in ecological channels with vegetation

Cited by 29 publications

References 27 publications

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Derivation of Optimized Equations for Estimation of Dispersion Coefficient in Natural Streams Using Hybridized ANN With PSO and CSO Algorithms

Improvements in the Explicit Estimation of Pollutant Dispersion Coefficient in Rivers by Subset Selection of Maximum Dissimilarity Hybridized With ANFIS-Firefly Algorithm (FFA)

A review of applications of fractional advection–dispersion equations for anomalous solute transport in surface and subsurface water

Contact Info

Product

Resources

About