Cover Image

Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia

The aim of this study is to develop a flood prediction model by analyzing the real-time flood parameters for Pengkalan Rama, Melaka river hereafter known as Sungai Melaka using the Box-Jenkins method. Hourly water levels are predicted to alleviate flood related problems caused by the overflow of Sun...

Full description

Saved in:
Bibliographic Details
Main Authors: Wong, Wei Ming, Subramaniam, Siva Kumar, Feroz, Farah Shahnaz, M. Subramaniam, Indra Devi, Lew, Rose Ai Fen
Format: Article
Language:English
Published: World Academy of Research in Science and Engineering 2020
Online Access:http://eprints.utem.edu.my/id/eprint/25229/2/IJATCSE160942020.PDF
http://eprints.utem.edu.my/id/eprint/25229/
http://www.warse.org/IJATCSE/static/pdf/file/ijatcse160942020.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utem.eprints.25229
record_format eprints
spelling my.utem.eprints.252292021-08-19T09:03:13Z http://eprints.utem.edu.my/id/eprint/25229/ Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia Wong, Wei Ming Subramaniam, Siva Kumar Feroz, Farah Shahnaz M. Subramaniam, Indra Devi Lew, Rose Ai Fen The aim of this study is to develop a flood prediction model by analyzing the real-time flood parameters for Pengkalan Rama, Melaka river hereafter known as Sungai Melaka using the Box-Jenkins method. Hourly water levels are predicted to alleviate flood related problems caused by the overflow of Sungai Melaka.. The time series from 7 January 2020 12.00 am until 15 January 2020 8.00 am was used to check the stationarity by using the Augmented Dickey-Fuller (ADF) and differencing method to make a non-stationary time series stationary. The main methods used for model identification with autocorrelation (ACF) function and partial autocorrelation function (PACF) are visual observation of the series. The best ARIMA model was identified by the parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The best ARIMA model for the Pengkalan Rama was ARIMA (2, 1, 2) with the AIC value 1297.5 and BIC value 1304.6. The time series had lead forecast up to 8 hours generated by using the ARIMA (2, 1, 2) model. The accuracy of the model was checked by comparing the original series and forecast series. The result of this research indicated that the ARIMA model is adequate for Sungai Melaka. In conclusion, ARIMA model is an adequate short term forecast of water level with the lead forecast of up to 8 hours. Hence, it is indubitable that the ARIMA model is suitable for river flood. World Academy of Research in Science and Engineering 2020-08 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/25229/2/IJATCSE160942020.PDF Wong, Wei Ming and Subramaniam, Siva Kumar and Feroz, Farah Shahnaz and M. Subramaniam, Indra Devi and Lew, Rose Ai Fen (2020) Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4). pp. 5287-5295. ISSN 2278-3091 http://www.warse.org/IJATCSE/static/pdf/file/ijatcse160942020.pdf 10.30534/IJATCSE/2020/160942020
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description The aim of this study is to develop a flood prediction model by analyzing the real-time flood parameters for Pengkalan Rama, Melaka river hereafter known as Sungai Melaka using the Box-Jenkins method. Hourly water levels are predicted to alleviate flood related problems caused by the overflow of Sungai Melaka.. The time series from 7 January 2020 12.00 am until 15 January 2020 8.00 am was used to check the stationarity by using the Augmented Dickey-Fuller (ADF) and differencing method to make a non-stationary time series stationary. The main methods used for model identification with autocorrelation (ACF) function and partial autocorrelation function (PACF) are visual observation of the series. The best ARIMA model was identified by the parameter Akaike Information Information Criterion (AIC) and the Bayesian Information Criterion (BIC). The best ARIMA model for the Pengkalan Rama was ARIMA (2, 1, 2) with the AIC value 1297.5 and BIC value 1304.6. The time series had lead forecast up to 8 hours generated by using the ARIMA (2, 1, 2) model. The accuracy of the model was checked by comparing the original series and forecast series. The result of this research indicated that the ARIMA model is adequate for Sungai Melaka. In conclusion, ARIMA model is an adequate short term forecast of water level with the lead forecast of up to 8 hours. Hence, it is indubitable that the ARIMA model is suitable for river flood.
format Article
author Wong, Wei Ming
Subramaniam, Siva Kumar
Feroz, Farah Shahnaz
M. Subramaniam, Indra Devi
Lew, Rose Ai Fen
spellingShingle Wong, Wei Ming
Subramaniam, Siva Kumar
Feroz, Farah Shahnaz
M. Subramaniam, Indra Devi
Lew, Rose Ai Fen
Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia
author_facet Wong, Wei Ming
Subramaniam, Siva Kumar
Feroz, Farah Shahnaz
M. Subramaniam, Indra Devi
Lew, Rose Ai Fen
author_sort Wong, Wei Ming
title Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia
title_short Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia
title_full Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia
title_fullStr Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia
title_full_unstemmed Flood Prediction Using ARIMA Model In Sungai Melaka, Malaysia
title_sort flood prediction using arima model in sungai melaka, malaysia
publisher World Academy of Research in Science and Engineering
publishDate 2020
url http://eprints.utem.edu.my/id/eprint/25229/2/IJATCSE160942020.PDF
http://eprints.utem.edu.my/id/eprint/25229/
http://www.warse.org/IJATCSE/static/pdf/file/ijatcse160942020.pdf
_version_ 1709671930009747456
score 13.211869

Similar Items