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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| Format: | Conference or Workshop Item |
| Language: | en |
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Institute of Physics Publishing
2020
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| 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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| _version_ | 1831529037144522752 |
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| 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 |
| building | UMPSA Library |
| collection | Institutional Repository |
| content_provider | Universiti Malaysia Pahang Al-Sultan Abdullah |
| content_source | UMPSA Institutional Repository |
| continent | Asia |
| country | 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 |
| id | my.ump.umpir.36007 |
| institution | Universiti Malaysia Pahang |
| language | en |
| publishDate | 2020 |
| publisher | Institute of Physics Publishing |
| record_format | eprints |
| 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 (Published) https://doi.org/10.1088/1757-899X/736/2/022108 |
| 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) |
| title | 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_short | 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) |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) |
| 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 |
| url_provider | http://umpir.ump.edu.my/ |