Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique

Mining has become one of the most challenging phenomena that can cause serious sedimentation process and aggregation problems in rivers in Malaysia. The process will increase the amount of sediment load in rivers that can result in them becoming shallow and subject to flood problems during heavy sto...

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Main Authors:	Nadiatul Adilah, Ahmad Abdul Ghani, Junaidah, A.
Format:	Conference or Workshop Item
Language:	English
Published:	2018
Subjects:	TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/23175/ http://umpir.ump.edu.my/id/eprint/23175/1/ICWR-2018_paper_93.pdf

id	ump-23175
recordtype	eprints
spelling	ump-231752019-01-01T00:03:14Z http://umpir.ump.edu.my/id/eprint/23175/ Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, A. TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering Mining has become one of the most challenging phenomena that can cause serious sedimentation process and aggregation problems in rivers in Malaysia. The process will increase the amount of sediment load in rivers that can result in them becoming shallow and subject to flood problems during heavy storms. In such a problem, management cost to monitor the amount of sediment in rivers can be really high. A new model using Evolutionary Polynomial Technique has been developed to assist researchers to predict sediment load in sandy rivers, in view of Malaysia. The model utilises 273 data of twelve rivers in Malaysia. Based on these data, it has been found that 173 is suitable for use in the model, that later gave 100% results within the range of the required difference ratio of 0.5 to 2.0. A new set of data that contains 82 data is used for validation and the results also give a good prediction of sediment loadings. The new model predictions are also compared with the present sediment model using other techniques, such as the regression technique by Ariffin in 2004, a modified Graf by Chan in 2005 and a multiple regression technique by Sinnakaudan in 2006. As a conclusion, the model developed using the Evolutionary Polynomial Technique indicates performance which gave the best results compared to the other three models; with a 100% accuracy within the stipulated range, followed by the multiple regression technique with 90.24%, the regression technique with 69.51% and finally the modified Graf with 23.17%. 2018-11-27 Conference or Workshop Item NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/23175/1/ICWR-2018_paper_93.pdf Nadiatul Adilah, Ahmad Abdul Ghani and Junaidah, A. (2018) Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique. In: 4th International Conferance on Water Resources (ICWR2019), 27-28 November 2018 , Langkawi, Kedah, Malaysia. pp. 1-10.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TC Hydraulic engineering. Ocean engineering Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, A. Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique
description	Mining has become one of the most challenging phenomena that can cause serious sedimentation process and aggregation problems in rivers in Malaysia. The process will increase the amount of sediment load in rivers that can result in them becoming shallow and subject to flood problems during heavy storms. In such a problem, management cost to monitor the amount of sediment in rivers can be really high. A new model using Evolutionary Polynomial Technique has been developed to assist researchers to predict sediment load in sandy rivers, in view of Malaysia. The model utilises 273 data of twelve rivers in Malaysia. Based on these data, it has been found that 173 is suitable for use in the model, that later gave 100% results within the range of the required difference ratio of 0.5 to 2.0. A new set of data that contains 82 data is used for validation and the results also give a good prediction of sediment loadings. The new model predictions are also compared with the present sediment model using other techniques, such as the regression technique by Ariffin in 2004, a modified Graf by Chan in 2005 and a multiple regression technique by Sinnakaudan in 2006. As a conclusion, the model developed using the Evolutionary Polynomial Technique indicates performance which gave the best results compared to the other three models; with a 100% accuracy within the stipulated range, followed by the multiple regression technique with 90.24%, the regression technique with 69.51% and finally the modified Graf with 23.17%.
format	Conference or Workshop Item
author	Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, A.
author_facet	Nadiatul Adilah, Ahmad Abdul Ghani Junaidah, A.
author_sort	Nadiatul Adilah, Ahmad Abdul Ghani
title	Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique
title_short	Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique
title_full	Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique
title_fullStr	Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique
title_full_unstemmed	Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique
title_sort	sediment load prediction for rivers in malaysia using evolutionary polynomial regression technique
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/23175/ http://umpir.ump.edu.my/id/eprint/23175/1/ICWR-2018_paper_93.pdf
first_indexed	2023-09-18T22:34:37Z
last_indexed	2023-09-18T22:34:37Z
_version_	1777416511989743616

Sediment load prediction for rivers in Malaysia using evolutionary polynomial regression technique

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