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Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia

An accurate river water level prediction model is vital for the development of flood mitigation plans in a river basin, and the accuracy of input data is important for ensuring good predictions. In this research study, a Support Vector Regression (SVR) model was applied to predict river water levels...

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Main Author: Tiu, Ervin Shan Khai
Format: Final Year Project / Dissertation / Thesis
Published: 2019
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utar.edu.my/3633/1/ESA%2D2019%2D1607563%2D1.pdf
http://eprints.utar.edu.my/3633/
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spelling my-utar-eprints.36332019-12-17T10:29:12Z Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia Tiu, Ervin Shan Khai TA Engineering (General). Civil engineering (General) An accurate river water level prediction model is vital for the development of flood mitigation plans in a river basin, and the accuracy of input data is important for ensuring good predictions. In this research study, a Support Vector Regression (SVR) model was applied to predict river water levels at the Dungun River, Terengganu, Malaysia. A major challenge of this study is to process the observed rainfall series which may tend to be incomplete and inconsistent. A better observed rainfall series data can lead to improved performance of the river water level prediction model. This study adopted the data pre-processing techniques to first improve the observed rainfall series. Three data pre-processing techniques namely; the Variational Mode Decomposition (VMD) method, the Boosting method and the Boosting-VMD method, were adopted to pre-process the observed rainfall series at the basin prior to applying them into the prediction model. Rainfall series and river water levels from November to February (Northeast Monsoon period) for the period of 1996-2016 were used. The three pre-processed rainfall series and the original observed rainfall series were then separately and individually, used to predict river water levels using the SVR model. The predicted river water levels from the four modified SVR models namely; the Ori-SVR (using original observed rainfall) model, the VMD-SVR (using VMD method) model, the B-SVR (using Boosting method) model, and the B-VMD-SVR (using Boosting-VMD method) model, were assessed against the observed river water levels, using non-parametric statistics (Model prediction errors’ range, standard deviation and confidence interval range) and parametric statistics (Bias, Root-Mean-Square Error, Mean Absolute Percentage Error, Nash-Sutcliffe Efficiency Coefficient and Mean Absolute Error) where appropriate, to assess the three data pre-processing techniques in enhancing the SVR model. Results indicated that the VMD, the Boosting and the Boosting-VMD methods afforded data improvements that boosted the performance of SVR models. Further statistical analyses showed that the SVR model cum the Boosting-VMD method that is, the (B-VMD-SVR) to be the most robust, with results of model prediction error’s range (4.27; Min: -2.95, Max: 1.32), model prediction error’s standard deviation (0.4200), model prediction error’s confidence interval range ([-0.02, 0.01]; Standard error: 0.00868), Bias (0.00), Root-Mean-Square Error (0.42), Mean Absolute Percentage Error (4.36), Nash-Sutcliffe Efficiency Coefficient (0.96) and Mean Absolute Error (0.28). 2019 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/3633/1/ESA%2D2019%2D1607563%2D1.pdf Tiu, Ervin Shan Khai (2019) Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia. Master dissertation/thesis, UTAR. http://eprints.utar.edu.my/3633/
institution Universiti Tunku Abdul Rahman
building UTAR Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tunku Abdul Rahman
content_source UTAR Institutional Repository
url_provider http://eprints.utar.edu.my
topic TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Tiu, Ervin Shan Khai
Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia
description An accurate river water level prediction model is vital for the development of flood mitigation plans in a river basin, and the accuracy of input data is important for ensuring good predictions. In this research study, a Support Vector Regression (SVR) model was applied to predict river water levels at the Dungun River, Terengganu, Malaysia. A major challenge of this study is to process the observed rainfall series which may tend to be incomplete and inconsistent. A better observed rainfall series data can lead to improved performance of the river water level prediction model. This study adopted the data pre-processing techniques to first improve the observed rainfall series. Three data pre-processing techniques namely; the Variational Mode Decomposition (VMD) method, the Boosting method and the Boosting-VMD method, were adopted to pre-process the observed rainfall series at the basin prior to applying them into the prediction model. Rainfall series and river water levels from November to February (Northeast Monsoon period) for the period of 1996-2016 were used. The three pre-processed rainfall series and the original observed rainfall series were then separately and individually, used to predict river water levels using the SVR model. The predicted river water levels from the four modified SVR models namely; the Ori-SVR (using original observed rainfall) model, the VMD-SVR (using VMD method) model, the B-SVR (using Boosting method) model, and the B-VMD-SVR (using Boosting-VMD method) model, were assessed against the observed river water levels, using non-parametric statistics (Model prediction errors’ range, standard deviation and confidence interval range) and parametric statistics (Bias, Root-Mean-Square Error, Mean Absolute Percentage Error, Nash-Sutcliffe Efficiency Coefficient and Mean Absolute Error) where appropriate, to assess the three data pre-processing techniques in enhancing the SVR model. Results indicated that the VMD, the Boosting and the Boosting-VMD methods afforded data improvements that boosted the performance of SVR models. Further statistical analyses showed that the SVR model cum the Boosting-VMD method that is, the (B-VMD-SVR) to be the most robust, with results of model prediction error’s range (4.27; Min: -2.95, Max: 1.32), model prediction error’s standard deviation (0.4200), model prediction error’s confidence interval range ([-0.02, 0.01]; Standard error: 0.00868), Bias (0.00), Root-Mean-Square Error (0.42), Mean Absolute Percentage Error (4.36), Nash-Sutcliffe Efficiency Coefficient (0.96) and Mean Absolute Error (0.28).
format Final Year Project / Dissertation / Thesis
author Tiu, Ervin Shan Khai
author_facet Tiu, Ervin Shan Khai
author_sort Tiu, Ervin Shan Khai
title Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia
title_short Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia
title_full Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia
title_fullStr Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia
title_full_unstemmed Data Pre-Processing Techniques For Improving River Water Level Prediction: A Case Study Of The Dungun River, Terengganu, Malaysia
title_sort data pre-processing techniques for improving river water level prediction: a case study of the dungun river, terengganu, malaysia
publishDate 2019
url http://eprints.utar.edu.my/3633/1/ESA%2D2019%2D1607563%2D1.pdf
http://eprints.utar.edu.my/3633/
_version_ 1657491982066384896
score 13.211869

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