Browsing by Author "Ab Ghani, Aminuddin"

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Citation - WoS: 17
Citation - Scopus: 17
Regression models for sediment transport in tropical rivers
(SPRINGER HEIDELBERG, 2021) Mohd Afiq Harun; Mir Jafar Sadegh Safari; Enes Gul; Aminuddin Ab Ghani; Safari, Mir Jafar Sadegh; Gul, Enes; Ab Ghani, Aminuddin; Harun, Mohd Afiq
The investigation of sediment transport in tropical rivers is essential for planning effective integrated river basin management to predict the changes in rivers. The characteristics of rivers and sediment in the tropical region are different compared to those of the rivers in Europe and the USA where the median sediment size tends to be much more refined. The origins of the rivers are mainly tropical forests. Due to the complexity of determining sediment transport many sediment transport equations were recommended in the literature. However the accuracy of the prediction results remains low particularly for the tropical rivers. The majority of the existing equations were developed using multiple non-linear regression (MNLR). Machine learning has recently been the method of choice to increase model prediction accuracy in complex hydrological problems. Compared to the conventional MNLR method machine learning algorithms have advanced and can produce a useful prediction model. In this research three machine learning models namely evolutionary polynomial regression (EPR) multi-gene genetic programming (MGGP) and M5 tree model (M5P) were implemented to model sediment transport for rivers in Malaysia. The formulated variables for the prediction model were originated from the revised equations reported in the relevant literature for Malaysian rivers. Among the three machine learning models in terms of different statistical measurement criteria EPR gives the best prediction model followed by MGGP and M5P. Machine learning is excellent at improving the prediction distribution of high data values but lacks accuracy compared to observations of lower data values. These results indicate that further study needs to be done to improve the machine learning model's accuracy to predict sediment transport.
Citation - WoS: 3
Citation - Scopus: 5
Sediment Incipient Motion in Sewer with a Bed Deposit
(Turkish Chamber Civil Engineers, 2022) Mir Jafar Sadegh Safari; Wan Hanna Melini Wan Mohtar; Charles Bong Hin Joo; Aminuddin AB. GHANI; Aizat Mohd Taib; Haitham Afan; Ahmed El-Shafie; Taib, Aizat Mohd; Safari, Mir Jafar Sadegh; Ab Ghani, Aminuddin; Wan Mohtar, Wan Hanna Melini; Ab. Ghani, Aminuddin; Mohtar, Wan Hanna Melini Wan; Afan, Haitham Abdulmohsin; EL-SHAFIE, Ahmed; Joo, Charles Bong Hin; Ghanı, Aminuddin Ab.; Hin Joo Bong, Charles
This paper analyses experimental data on sediment incipient motion with varying sediment\rbed thickness (of d50 5 10 and 24 mm). Sediment particles (with sizes ranging from 0.5 mm\rto 4.78 mm) were used to evaluate the effect of deposited bed. Variation of shear velocity\restimation was investigated where the critical Shields parameter was expressed using bedslope product u∗cb log-law u∗cl and was extended in terms of critical mean velocity. The\rcritical Shields parameters obtained were significantly lower than the traditional Shields\rcurve when u∗cl was used compared to u∗cb. Higher critical mean velocity is needed for\rshallower deposits.