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
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Improving total sediment load prediction using genetic programming technique (Case Study_Malaysia).pdf Available under License Creative Commons Attribution. Download (612kB) | Preview |
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 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.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Prediction; Flood forecasting; Water tables |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Civil Engineering Technology |
| Depositing User: | Mr Muhamad Firdaus Janih@Jaini |
| Date Deposited: | 28 Dec 2022 03:14 |
| Last Modified: | 28 Dec 2022 03:14 |
| URI: | http://umpir.ump.edu.my/id/eprint/36007 |
| Download Statistic: | View Download Statistics |
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