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1860797973236023296
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| building |
INTELEK Repository
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| collection |
Online Access
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| collectionurl |
https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072
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| date |
2024-08-30 10:15:25
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| format |
Restricted Document
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| id |
15195
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UniSZA
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[1] Rizwan, A.M., L.Y.C. Dennis, and C. Liu. 2008. Journal of Environmental Science. 20: 120–128. DOI: 10.1016/s1001– 0742(08)60019–4. [2] Metcalfe, J.L. 1989. History and present status in Europe. Environ. Pollut. 60: 101–139. [3] Pinel-Alloul, B., G. Methot, L. Lapierre and A. Willsie. 1996. Environ. Pollut. 9: 65–87. [4] Nedeau, E.J., R.W. Merritt, and M.G. Kaufman. 2003. Environmental Pollution. 123(1): 1–13. [5] Dan’azumi, S., and M.H. Bichi. 2007. International Journal of Engineering & Technology IJET-IJENS. 10(01). [6] Juahir H., S.M. Zain, M.K. Yusoff, T.I.T. Hanidza, A.S.M. Armi, M.E.Toriman, and M. Mokhtar. 2011. Environ. Monitoring Assessment 173: 625–641. DOI: 10.1007/s10661-010-1411-x. [7] Mazlum, N., A. Ozer, and S. Mazlum. 1999. Turkish Journal. Engineering Environmental Science. 23: 19–26. [8] Juahir, H., M.Z. Sharifuddin, K.Y. Mohd, H.A.S. Tengku, A. Mohd, E.T. Mohd, and M. Mazlin. 2010. Environ Monit Assess. 173 (1–4): 625–41. DOI: 10.1007/s10661-010-1411-x. [9] Juahir, H., M.E. Toriman, S.M. Zain, M. Mokhtar, J. Zaihan, and M.J. Ijan Khushaida. 2008. American-Eurasian Journal of Agricultural & Environmental Sciences. 4(1): 258–265. [10] Floyd, F.J., and K.F. Widaman. 1995. Psychological Assessment. 7 (3): 286–299. [11] Juahir, H., M.Z. Sharifuddin, Z.A. Ahmad, K.Y. Mohd, and M. Mazlin. 2009. Journal of Environmental Monitoring. 12: 287–295. [12] Imrie, C.E, Durucan, S. and Korea A. 2000. J.Hydrol. 233: 138–153. [13] Herman, I. 1994. Selangor: Tekno Edar–Descriptive statistical analysis [14] Aitchison, J. 1986. The Statistical Analysis of Compositional Data. Chapman & Hall, London, United Kingdom
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5754-01-FH02-ESERI-15-02305.pdf
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Hadi Nur
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oai_dc
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https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15195
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| spelling |
15195 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=15195 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection407072 Restricted Document Article Journal application/pdf Adobe Acrobat Pro DC 20 Paper Capture Plug-in with ClearScan 6 1.6 Hadi Nur 2024-08-30 10:15:25 5754-01-FH02-ESERI-15-02305.pdf UniSZA Private Access Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin Jurnal Teknologi 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%. 72 1 Penerbit UTM Press Penerbit UTM Press 137-141 [1] Rizwan, A.M., L.Y.C. Dennis, and C. Liu. 2008. Journal of Environmental Science. 20: 120–128. DOI: 10.1016/s1001– 0742(08)60019–4. [2] Metcalfe, J.L. 1989. History and present status in Europe. Environ. Pollut. 60: 101–139. [3] Pinel-Alloul, B., G. Methot, L. Lapierre and A. Willsie. 1996. Environ. Pollut. 9: 65–87. [4] Nedeau, E.J., R.W. Merritt, and M.G. Kaufman. 2003. Environmental Pollution. 123(1): 1–13. [5] Dan’azumi, S., and M.H. Bichi. 2007. International Journal of Engineering & Technology IJET-IJENS. 10(01). [6] Juahir H., S.M. Zain, M.K. Yusoff, T.I.T. Hanidza, A.S.M. Armi, M.E.Toriman, and M. Mokhtar. 2011. Environ. Monitoring Assessment 173: 625–641. DOI: 10.1007/s10661-010-1411-x. [7] Mazlum, N., A. Ozer, and S. Mazlum. 1999. Turkish Journal. Engineering Environmental Science. 23: 19–26. [8] Juahir, H., M.Z. Sharifuddin, K.Y. Mohd, H.A.S. Tengku, A. Mohd, E.T. Mohd, and M. Mazlin. 2010. Environ Monit Assess. 173 (1–4): 625–41. DOI: 10.1007/s10661-010-1411-x. [9] Juahir, H., M.E. Toriman, S.M. Zain, M. Mokhtar, J. Zaihan, and M.J. Ijan Khushaida. 2008. American-Eurasian Journal of Agricultural & Environmental Sciences. 4(1): 258–265. [10] Floyd, F.J., and K.F. Widaman. 1995. Psychological Assessment. 7 (3): 286–299. [11] Juahir, H., M.Z. Sharifuddin, Z.A. Ahmad, K.Y. Mohd, and M. Mazlin. 2009. Journal of Environmental Monitoring. 12: 287–295. [12] Imrie, C.E, Durucan, S. and Korea A. 2000. J.Hydrol. 233: 138–153. [13] Herman, I. 1994. Selangor: Tekno Edar–Descriptive statistical analysis [14] Aitchison, J. 1986. The Statistical Analysis of Compositional Data. Chapman & Hall, London, United Kingdom
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| spellingShingle |
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
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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%.
|
| title |
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
|
| title_full |
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
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| title_fullStr |
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
|
| title_full_unstemmed |
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
|
| title_short |
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study In Kuantan River Basin
|
| title_sort |
flood risk pattern recognition using chemometric technique: a case study in kuantan river basin
|