Flood risk pattern recognition using chemometric technique: A case study in Kuantan River Basin

Ahmad Shakir Mohd Saudi, Hafizan Juahir, Azman Azid, Mohd Khairul Amri Kamarudin, Mohd Fadhil Kasim, Mohd. Ekhwan Toriman, Nor Azlina Abdul Aziz, Che Noraini Che Hasnam, Mohd Saiful Samsudin

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

Original languageEnglish
Pages (from-to)137-141
Number of pages5
JournalJurnal Teknologi
Volume72
Issue number1
DOIs
Publication statusPublished - 2015
Externally publishedYes

Fingerprint

Stream flow
Water levels
Catchments
Pattern recognition
Rivers
Neural networks
Flow of solids
Time series analysis
Factor analysis

Keywords

  • Artificial neural network
  • Factor analysis
  • Integrated chemometric
  • Time series analysis

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Saudi, A. S. M., Juahir, H., Azid, A., Kamarudin, M. K. A., Kasim, M. F., Toriman, M. E., ... Samsudin, M. S. (2015). Flood risk pattern recognition using chemometric technique: A case study in Kuantan River Basin. Jurnal Teknologi, 72(1), 137-141. https://doi.org/10.11113/jt.v72.3013

Flood risk pattern recognition using chemometric technique : A case study in Kuantan River Basin. / Saudi, Ahmad Shakir Mohd; Juahir, Hafizan; Azid, Azman; Kamarudin, Mohd Khairul Amri; Kasim, Mohd Fadhil; Toriman, Mohd. Ekhwan; Aziz, Nor Azlina Abdul; Hasnam, Che Noraini Che; Samsudin, Mohd Saiful.

In: Jurnal Teknologi, Vol. 72, No. 1, 2015, p. 137-141.

Research output: Contribution to journalArticle

Saudi, ASM, Juahir, H, Azid, A, Kamarudin, MKA, Kasim, MF, Toriman, ME, Aziz, NAA, Hasnam, CNC & Samsudin, MS 2015, 'Flood risk pattern recognition using chemometric technique: A case study in Kuantan River Basin', Jurnal Teknologi, vol. 72, no. 1, pp. 137-141. https://doi.org/10.11113/jt.v72.3013
Saudi, Ahmad Shakir Mohd ; Juahir, Hafizan ; Azid, Azman ; Kamarudin, Mohd Khairul Amri ; Kasim, Mohd Fadhil ; Toriman, Mohd. Ekhwan ; Aziz, Nor Azlina Abdul ; Hasnam, Che Noraini Che ; Samsudin, Mohd Saiful. / Flood risk pattern recognition using chemometric technique : A case study in Kuantan River Basin. In: Jurnal Teknologi. 2015 ; Vol. 72, No. 1. pp. 137-141.
@article{2676b4315198441481f3fc653b0d9193,
title = "Flood risk pattern recognition using chemometric technique: A case study in Kuantan River Basin",
abstract = "Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8{\%}.",
keywords = "Artificial neural network, Factor analysis, Integrated chemometric, Time series analysis",
author = "Saudi, {Ahmad Shakir Mohd} and Hafizan Juahir and Azman Azid and Kamarudin, {Mohd Khairul Amri} and Kasim, {Mohd Fadhil} and Toriman, {Mohd. Ekhwan} and Aziz, {Nor Azlina Abdul} and Hasnam, {Che Noraini Che} and Samsudin, {Mohd Saiful}",
year = "2015",
doi = "10.11113/jt.v72.3013",
language = "English",
volume = "72",
pages = "137--141",
journal = "Jurnal Teknologi",
issn = "0127-9696",
publisher = "Penerbit Universiti Teknologi Malaysia",
number = "1",

}

TY - JOUR

T1 - Flood risk pattern recognition using chemometric technique

T2 - A case study in Kuantan River Basin

AU - Saudi, Ahmad Shakir Mohd

AU - Juahir, Hafizan

AU - Azid, Azman

AU - Kamarudin, Mohd Khairul Amri

AU - Kasim, Mohd Fadhil

AU - Toriman, Mohd. Ekhwan

AU - Aziz, Nor Azlina Abdul

AU - Hasnam, Che Noraini Che

AU - Samsudin, Mohd Saiful

PY - 2015

Y1 - 2015

N2 - Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

AB - Integrated Chemometric and Artificial Neural Network were being applied in this study to identify the main contributor for flood, predicting hydrological modelling and risk of flood occurrence at the Kuantan river basin. Based on the Correlation Test analysis, the relationship for Suspended Solid and Stream Flow with Water Level were very high with Pearson correlation of coefficient value more than 0.5. Factor Analysis had been carried out and based on the result, variables such as Stream Flow, Suspended Solid and Water Level turned out to be the major factors and had a strong factor pattern with the results of factor score with >0.7 respectively. Time series analysis was being employed and the limitation had been set up where the Upper Control Limit for Stream Flow, Suspended Solid and Water Level where at this level, it was predicted by using Artificial Neural Network (ANN) to be High Risk Class. The accuracy of prediction from this method stood at 97.8%.

KW - Artificial neural network

KW - Factor analysis

KW - Integrated chemometric

KW - Time series analysis

UR - http://www.scopus.com/inward/record.url?scp=84920002382&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920002382&partnerID=8YFLogxK

U2 - 10.11113/jt.v72.3013

DO - 10.11113/jt.v72.3013

M3 - Article

AN - SCOPUS:84920002382

VL - 72

SP - 137

EP - 141

JO - Jurnal Teknologi

JF - Jurnal Teknologi

SN - 0127-9696

IS - 1

ER -