Stable channel analysis with sediment transport for rivers in Malaysia: A case study of the Muda, Kurau, and Langat rivers

Harun, Mohd Afiq; Ghani, Aminuddin Ab.; Mohammadpour, Reza; Chan, Ngai Weng

doi:10.1016/j.ijsrc.2020.03.008

Cited by 10 publications

(2 citation statements)

References 33 publications

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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%

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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Section: Development Of New Equations For Stable Channel Geometrymentioning

confidence: 99%

GEP- and MLR-based equations for stable channel analysis

Harun

Ghani

Mohammadpour

et al. 2021

Journal of Hydroinformatics

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“…High‐quality water and low‐cost treatment technologies are becoming important criteria for upgrading existing water treatment plants (Mohammadpour et al ., 2016; Harun et al ., 2020). The most likely technology to be applied is the combination of some or all of the following: coagulation, flocculation and sedimentation.…”

Section: Introductionmentioning

confidence: 99%

Comparing the log‐response curve and adsorption isotherm model for removing dissolved organic matter during La Nina event

Murshed

Mohammadpour

Chow

2020

Water & Environment J

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Coagulation is the most commonly used chemical treatment process that is used to remove contaminants from drinking water. In this study, the Log‐Dose‐Response (LDR) model and theoretical isotherm models (Langmuir and Freundlich) were employed to predict the DOC removal in the coagulation process during La Nina events. The six months water samples were collected during an intense La Niña cycle at various locations along the River Murray and elsewhere in South Australia. The Jar testing was conducted with different Alum dose and pH levels, that is, at 5.5, 6.0 and 7.0. The result indicates that the optimum dose of Alum for DOC removal is 15.0 mg/L and the LDR model is the best one for predicting the DOC removal in the coagulation process. The mean of standard error for this model is less than 5% for all pHs compared to Langmuir and Freundlich predictions with a value of 11% and 8%, respectively. Moreover, the standard deviation for the LDR model was low at 2.49, 1.22 and 0.75 for pH 5.5, pH 6.0 and pH 7.0, respectively.

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