Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes

Ebtehaj, Isa; Bonakdari, Hossein; Safari, Mir Jafar Sadegh; Gharabaghi, Bahram; Zaji, Amir Hossein; Madavar, Hossien Riahi; Khozani, Zohreh Sheikh; Es-haghi, Mohammad Sadegh; Shishegaran, Aydin; Mehr, Ali Danandeh

doi:10.1016/j.ijsrc.2019.08.005

Cited by 54 publications

(18 citation statements)

References 42 publications

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“…In the current study the presented empirical equations in Table 1 are compared with the results of explicit optimized equations based on ANN-CSO, ANN-PSO. The results of all 36 models are evaluated by the use of the RMSE, mean absolute error (MAE), Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R 2 ), index of agreement (d), persistence index (PI), confidence index (CI), and relative absolute error (RAE).The formulation and detailed description of these statistical indices are presented elsewhere [37,56,57,58,59,60]. Furthermore, scatter plots, trend comparisons, box plot of errors, Taylor diagrams and error frequency distributions were used for graphical verification of model results.…”

Section: Hybridization Framework and Evaluationmentioning

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

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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.

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Section: Hybridization Framework and Evaluationmentioning

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

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“…Researchers have divided the significant parameters that contribute to sediment transport into four-parameter classes, namely, mobility, transport, sediment and flow resistance (Ebtehaj & Bonakdari 2016;Harun et al 2020). This concept has been widely used by researchers to define the sediment transport in the river (Sinnakaudan et al 2006;Harun et al 2020), in closed channels or in pipes (Ebtehaj et al 2019;Danandeh Mehr & Safari 2020). These parameters were also found to be prevalent in the application of sewer sedimentation analysis Kargar et al 2019).…”

Section: Development Of New Equations For Stable Channel Geometrymentioning

confidence: 99%

“…This method was extensively investigated by Gholami et al (2019aGholami et al ( , 2019b and Shaghaghi et al (2018) for use in a stable channel. Machine learning can be applied by using various methods such as artificial neural networks, adaptive network-based fuzzy inference systems, support vector machines, gene expression programming (GEP) and evolutionary polynomial regression (EPR) (Giustolisi & Savic 2009;Ebtehaj et al 2019;Khosravi & Javan 2019;Roushangar & Ghasempour 2019;Yahaya 2019;Asheghi et al 2020;Najafzadeh & Oliveto 2020). The research done by Bonakdari et al (2020) showed that GEP and EPR had improved the accuracy of the model prediction; however, the improvement is not remarkable compared to the original equation.…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

GEP- and MLR-based equations for stable channel analysis

Harun

Ghani

Mohammadpour

et al. 2021

Journal of Hydroinformatics

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For decades, research on stable channel hydraulic geometry was based on the following parameters: river discharge, dimensionless discharge, the median size of bed material and the slope. Although significant research has been conducted in this area, including applied machine learning to increase the geometry model prediction accuracy, there has been no remarkable improvement as the variables used to describe the geometry relationship remain the same. The novelty of this study is demonstrated by the parameters used in the stable channel geometry equations that outperform the existing equation's accuracy. In this research, sediment transport parameters are introduced and analysed by applying the multiple linear regression (MLR) and gene expression programming (GEP) methods. The new equation of the width, depth and bed slope can give much-improved results in efficiency and lower errors. Furthermore, a new parameter B/y is introduced in this study to solve the restriction issue, either in width or depth prediction. The results from MLR and GEP show that in addition to the existing hydraulic geometry parameter, the B/y parameter is also able to give high accuracy results for width and depth predictions. Both calibration and validation for the B/y parameter yield high R2 and NSE values with low mean squared errors and mean absolute errors.

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“…In this study, the data set was divided into 70% training and 30% testing data. Based on the findings published by [40], who performed uncertainty analysis, the split between training…”

Section: Plos Onementioning

confidence: 99%

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms

et al. 2021

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To reduce the problem of sedimentation in open channels, calculating flow velocity is critical. Undesirable operating costs arise due to sedimentation problems. To overcome these problems, the development of machine learning based models may provide reliable results. Recently, numerous studies have been conducted to model sediment transport in non-deposition condition however, the main deficiency of the existing studies is utilization of a limited range of data in model development. To tackle this drawback, six data sets with wide ranges of pipe size, volumetric sediment concentration, channel bed slope, sediment size and flow depth are used for the model development in this study. Moreover, two tree-based algorithms, namely M5 rule tree (M5RT) and M5 regression tree (M5RGT) are implemented, and results are compared to the traditional regression equations available in the literature. The results show that machine learning approaches outperform traditional regression models. The tree-based algorithms, M5RT and M5RGT, provided satisfactory results in contrast to their regression-based alternatives with RMSE = 1.184 and RMSE = 1.071, respectively. In order to recommend a practical solution, the tree structure algorithms are supplied to compute sediment transport in an open channel flow.

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Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes

Cited by 54 publications

References 42 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

GEP- and MLR-based equations for stable channel analysis

Sediment transport modeling in non-deposition with clean bed condition using different tree-based algorithms

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