Abstract
Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.
| Original language | English |
|---|---|
| Pages (from-to) | 165-170 |
| Number of pages | 6 |
| Journal | Jurnal Teknologi |
| Volume | 74 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2015 |
| Externally published | Yes |
Fingerprint
Keywords
- Artificial neural network
- Factor analysis
- Flood
- Monsoon
- Statistical process control
- Time series analysis
ASJC Scopus subject areas
- Engineering(all)
Cite this
Flood risk pattern recognition using integrated chemometric method and artificial neural network : A case study in the Johor River Basin. / Saudi, Ahmad Shakir Mohd; Azid, Azman; Juahir, Hafizan; Toriman, Mohd. Ekhwan; Amran, Mohammad Azizi; Mustafa, Ahmad Dasuki; Azaman, Fazureen; Kamarudin, Mohd Khairul Amri; Saudi, Madihah Mohd.
In: Jurnal Teknologi, Vol. 74, No. 1, 2015, p. 165-170.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Flood risk pattern recognition using integrated chemometric method and artificial neural network
T2 - A case study in the Johor River Basin
AU - Saudi, Ahmad Shakir Mohd
AU - Azid, Azman
AU - Juahir, Hafizan
AU - Toriman, Mohd. Ekhwan
AU - Amran, Mohammad Azizi
AU - Mustafa, Ahmad Dasuki
AU - Azaman, Fazureen
AU - Kamarudin, Mohd Khairul Amri
AU - Saudi, Madihah Mohd
PY - 2015
Y1 - 2015
N2 - Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.
AB - Flood is a major problem in Johor river basin, which normally happened during monsoon season. However in this study, it shows that rainfall did not have a strong relationship for the changes of water level compared to suspended solid and stream flow, where both variables have p-values of <0.0001 and these variables also became the main factors in contributing to the flood occurrence based on Factor Analysis result. Time Series Analysis was being carried out and based on Statistical Process Control, the limitation has been set up for mitigation in controlling flood. All data beyond the Upper Control Limit was predicted to have High Risk to face flood and Emergency Response Plan should be implemented to prevent complication and destruction because of flood. The prediction for the risk level was carried out using the application of Artificial Neural Network (ANN), where the accuracy of prediction was very high, with the result of 96% for the level of accuracy in the prediction of risk class.
KW - Artificial neural network
KW - Factor analysis
KW - Flood
KW - Monsoon
KW - Statistical process control
KW - Time series analysis
UR - http://www.scopus.com/inward/record.url?scp=84928606486&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84928606486&partnerID=8YFLogxK
U2 - 10.11113/jt.v74.3772
DO - 10.11113/jt.v74.3772
M3 - Article
AN - SCOPUS:84928606486
VL - 74
SP - 165
EP - 170
JO - Jurnal Teknologi
JF - Jurnal Teknologi
SN - 0127-9696
IS - 1
ER -