Cover Image

Flood forecasting of Malaysia Kelantan river using support vector regression technique

The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This stu...

Full description

Saved in:
Bibliographic Details
Main Authors: Faruq, Amrul, Marto, Aminaton, Abdullah, Shahrum Shah
Format: Article
Language:English
Published: Tech Science Press 2021
Subjects:
GB Physical geography
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.utm.my/id/eprint/94067/1/AminatonMarto2021_FloodForecastingofMalaysiaKelantan.pdf
http://eprints.utm.my/id/eprint/94067/
http://dx.doi.org/10.32604/CSSE.2021.017468
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.94067
record_format eprints
spelling my.utm.940672022-02-28T13:31:36Z http://eprints.utm.my/id/eprint/94067/ Flood forecasting of Malaysia Kelantan river using support vector regression technique Faruq, Amrul Marto, Aminaton Abdullah, Shahrum Shah GB Physical geography TA Engineering (General). Civil engineering (General) The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learningbased technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were identified as input variables. A river water level in downstream area was selected as output of flood forecasting model. A comparison with several benchmarking models, including radial basis function (RBF) and nonlinear autoregressive with exogenous input (NARX) neural network was performed. The results demonstrated that in terms of RMSE error, NARX model was better for the proposed models. However, support vector regression (SVR) demonstrated a more consistent performance, indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time. The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems. Tech Science Press 2021-03 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/94067/1/AminatonMarto2021_FloodForecastingofMalaysiaKelantan.pdf Faruq, Amrul and Marto, Aminaton and Abdullah, Shahrum Shah (2021) Flood forecasting of Malaysia Kelantan river using support vector regression technique. Computer Systems Science and Engineering, 39 (3). pp. 297-306. ISSN 0267-6192 http://dx.doi.org/10.32604/CSSE.2021.017468 DOI:10.32604/CSSE.2021.017468
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic GB Physical geography
TA Engineering (General). Civil engineering (General)
spellingShingle GB Physical geography
TA Engineering (General). Civil engineering (General)
Faruq, Amrul
Marto, Aminaton
Abdullah, Shahrum Shah
Flood forecasting of Malaysia Kelantan river using support vector regression technique
description The rainstorm is believed to contribute flood disasters in upstream catchments, resulting in further consequences in downstream area due to rise of river water levels. Forecasting for flood water level has been challenging, presenting complex task due to its nonlinearities and dependencies. This study proposes a support vector machine regression model, regarded as a powerful machine learningbased technique to forecast flood water levels in downstream area for different lead times. As a case study, Kelantan River in Malaysia has been selected to validate the proposed model. Four water level stations in river basin upstream were identified as input variables. A river water level in downstream area was selected as output of flood forecasting model. A comparison with several benchmarking models, including radial basis function (RBF) and nonlinear autoregressive with exogenous input (NARX) neural network was performed. The results demonstrated that in terms of RMSE error, NARX model was better for the proposed models. However, support vector regression (SVR) demonstrated a more consistent performance, indicated by the highest coefficient of determination value in twelve-hour period ahead of forecasting time. The findings of this study signified that SVR was more capable of addressing the long-term flood forecasting problems.
format Article
author Faruq, Amrul
Marto, Aminaton
Abdullah, Shahrum Shah
author_facet Faruq, Amrul
Marto, Aminaton
Abdullah, Shahrum Shah
author_sort Faruq, Amrul
title Flood forecasting of Malaysia Kelantan river using support vector regression technique
title_short Flood forecasting of Malaysia Kelantan river using support vector regression technique
title_full Flood forecasting of Malaysia Kelantan river using support vector regression technique
title_fullStr Flood forecasting of Malaysia Kelantan river using support vector regression technique
title_full_unstemmed Flood forecasting of Malaysia Kelantan river using support vector regression technique
title_sort flood forecasting of malaysia kelantan river using support vector regression technique
publisher Tech Science Press
publishDate 2021
url http://eprints.utm.my/id/eprint/94067/1/AminatonMarto2021_FloodForecastingofMalaysiaKelantan.pdf
http://eprints.utm.my/id/eprint/94067/
http://dx.doi.org/10.32604/CSSE.2021.017468
_version_ 1726791476004782080
score 13.244367

Similar Items