Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Penerbit UTM Press
2015
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/5754/1/FH02-ESERI-15-02305.pdf http://eprints.unisza.edu.my/5754/2/FH02-ESERI-15-02366.jpg http://eprints.unisza.edu.my/5754/ |
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| Summary: | 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%. |
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