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

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...

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Main Authors:	Nadiatul Adilah, Ahmad Abdul Ghani, Junaidah, Ariffin
Format:	Conference or Workshop Item
Language:	English
Published:	2017
Subjects:	T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/18901/ http://umpir.ump.edu.my/id/eprint/18901/1/Improving%20Total%20Sediment%20Load%20Prediction%20using1.pdf

id	ump-18901
recordtype	eprints
spelling	ump-189012018-07-30T07:05:35Z http://umpir.ump.edu.my/id/eprint/18901/ Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia) Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, Ariffin T Technology (General) 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. 2017 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/18901/1/Improving%20Total%20Sediment%20Load%20Prediction%20using1.pdf 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. .
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, Ariffin Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)
description	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.
format	Conference or Workshop Item
author	Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, Ariffin
author_facet	Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, Ariffin
author_sort	Nadiatul Adilah, Ahmad Abdul Ghani
title	Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)
title_short	Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)
title_full	Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)
title_fullStr	Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)
title_full_unstemmed	Improving Total Sediment Load Prediction using the GE Technique (Case Study: Malaysia)
title_sort	improving total sediment load prediction using the ge technique (case study: malaysia)
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/18901/ http://umpir.ump.edu.my/id/eprint/18901/1/Improving%20Total%20Sediment%20Load%20Prediction%20using1.pdf
first_indexed	2023-09-18T22:27:00Z
last_indexed	2023-09-18T22:27:00Z
_version_	1777416032793657344

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

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