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Improving total sediment load prediction using genetic programming 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 known as sedimen...

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Main Authors: N. A., Ahmad Abdul Ghani, N. A., Kamal, J., Ariffin
Format: Conference or Workshop Item
Language:English
Published: Institute of Physics Publishing 2020
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/36007/1/Improving%20total%20sediment%20load%20prediction%20using%20genetic%20programming%20technique%20%28Case%20Study_Malaysia%29.pdf
http://umpir.ump.edu.my/id/eprint/36007/
https://doi.org/10.1088/1757-899X/736/2/022108
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spelling my.ump.umpir.360072022-12-28T03:14:40Z http://umpir.ump.edu.my/id/eprint/36007/ Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia) N. A., Ahmad Abdul Ghani N. A., Kamal J., Ariffin T Technology (General) TA Engineering (General). Civil engineering (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 known as sediment transport model may not be valid to predict total sediment load of rivers in the tropics due to significant differences in the hydrological and sediment characteristics conditions. A new technique called Genetic programming (GP) technique is used to develop a new model to improve the prediction of total sediment load for rivers in tropical Malaysia. The new model named Evolutionary Polynomial Regression (EPR) model is compared with other three available sediment transport models using the different techniques including, Regression Equation, Modified Graf and Multiple Regression. Statistical analyses based on 82 data sets show the sediment transport model using this new technique perform well compare to other models. Institute of Physics Publishing 2020-03-04 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/36007/1/Improving%20total%20sediment%20load%20prediction%20using%20genetic%20programming%20technique%20%28Case%20Study_Malaysia%29.pdf N. A., Ahmad Abdul Ghani and N. A., Kamal and J., Ariffin (2020) Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia). In: IOP Conference Series: Materials Science and Engineering, Energy Security and Chemical Engineering Congress, 17-19 July 2019 , Kuala Lumpur, Malaysia. pp. 1-7., 736 (022108). ISSN 1757-8981 https://doi.org/10.1088/1757-899X/736/2/022108
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
N. A., Ahmad Abdul Ghani
N. A., Kamal
J., Ariffin
Improving total sediment load prediction using genetic programming 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 known as sediment transport model may not be valid to predict total sediment load of rivers in the tropics due to significant differences in the hydrological and sediment characteristics conditions. A new technique called Genetic programming (GP) technique is used to develop a new model to improve the prediction of total sediment load for rivers in tropical Malaysia. The new model named Evolutionary Polynomial Regression (EPR) model is compared with other three available sediment transport models using the different techniques including, Regression Equation, Modified Graf and Multiple Regression. Statistical analyses based on 82 data sets show the sediment transport model using this new technique perform well compare to other models.
format Conference or Workshop Item
author N. A., Ahmad Abdul Ghani
N. A., Kamal
J., Ariffin
author_facet N. A., Ahmad Abdul Ghani
N. A., Kamal
J., Ariffin
author_sort N. A., Ahmad Abdul Ghani
title Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia)
title_short Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia)
title_full Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia)
title_fullStr Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia)
title_full_unstemmed Improving total sediment load prediction using genetic programming technique (Case Study: Malaysia)
title_sort improving total sediment load prediction using genetic programming technique (case study: malaysia)
publisher Institute of Physics Publishing
publishDate 2020
url http://umpir.ump.edu.my/id/eprint/36007/1/Improving%20total%20sediment%20load%20prediction%20using%20genetic%20programming%20technique%20%28Case%20Study_Malaysia%29.pdf
http://umpir.ump.edu.my/id/eprint/36007/
https://doi.org/10.1088/1757-899X/736/2/022108
_version_ 1753788570300055552
score 13.211869

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