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Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)

Nadiatul Adilah, Ahmad Abdul Ghani and Junaidah, Ariffin (2017) Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia). In: Fluids and Chemical Engineering Conference (FluidsChE 2017), 4-6 April 2017 , Kota Kinabalu, Sabah, Malaysia. .

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Abstract

Predicted total sediment load is usually used to identify the intensity of a sedimentation process. Currently, the existing available models to predict total load are mostly developed based on data collected from flumes, channels and rivers located in western countries. These models may not be valid to predict sediment transport of rivers in the Tropics due to significant differences in the hydrological and sediment characteristics conditions. A new model using genetic programming (GE) technique is used to improve the prediction of sediment load for rivers in tropical Malaysia. Methods/StatisticalAnalysis: The model predictions are compared with those obtained from five available sediment transport models, including Engelund & Hansen (1967), Graf (1971), Ariffin (2004), Chan et al. (2005) and Sinnakaudan et al. (2006). Findings: The performance of the model in relation to the test set shows less scattering around the line of equality, between the measured and predicted total sediment loads. Statistical analyses of 68 data sets give the coefficient of correlation, r and the discrepancy ratio of 0.82 and 0.53 respectively. Application/Improvements: Hence, the GE Technique used in the prediction of Total Sediment Load is found to give better accuracy compared to other methods.

Item Type: Conference or Workshop Item (Lecture)
Uncontrolled Keywords: genetic programming technique, prediction, river, total sediment load
Subjects: T Technology > T Technology (General)
Faculty/Division: Faculty of Civil Engineering & Earth Resources
Depositing User: Mr. Mohd Safwan Rizal Saripudin
Date Deposited: 30 Jul 2018 07:05
Last Modified: 30 Jul 2018 07:05
URI: http://umpir.ump.edu.my/id/eprint/18901
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