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...
Saved in:
Main Author: | Tiu, Ervin Shan Khai |
---|---|
Format: | Final Year Project / Dissertation / Thesis |
Published: |
2019
|
Subjects: | |
Online Access: | http://eprints.utar.edu.my/3633/1/ESA%2D2019%2D1607563%2D1.pdf http://eprints.utar.edu.my/3633/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predictions of water level in Dungun River Terengganu using partial least squares regression
by: Ibrahim, Noraini, et al.
Published: (2012) -
Time series methods for water level forecasting of Dungun River in Terengganu Malaysia
by: Arbain, Siti Hajar, et al.
Published: (2012) -
An evaluation of various data pre-processing techniques with machine learning models for water level prediction
by: Tiu, Ervin Shan Khai, et al.
Published: (2022) -
River water quality monitoring using statistical process control in Dungun River Basin, Terengganu, Malaysia
by: Tengku Nurul Aimi Balqis Tengku Malim Busu,, et al.
Published: (2021) -
Water level prediction in tidal river (Sungai Muar, Johor)
by: Demun, Amat Sairin
Published: (1991)