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

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


Integrated chemometric, artificial neural network, factor analysis, time series analysis.

Full Text:

PDF

References


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.

Metcalfe, J.L. 1989. History and present status in Europe. Environ. Pollut. 60: 101–139.

Pinel-Alloul, B., G. Methot, L. Lapierre and A. Willsie. 1996. Environ. Pollut. 9: 65–87.

Nedeau, E.J., R.W. Merritt, and M.G. Kaufman. 2003. Environmental Pollution. 123(1): 1–13.

Dan’azumi, S., and M.H. Bichi. 2007. International Journal of Engineering & Technology IJET-IJENS. 10(01).

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.

Mazlum, N., A. Ozer, and S. Mazlum. 1999. Turkish Journal. Engineering Environmental Science. 23: 19–26.

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.

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.

Floyd, F.J., and K.F. Widaman. 1995. Psychological Assessment. 7 (3): 286–299.

Juahir, H., M.Z. Sharifuddin, Z.A. Ahmad, K.Y. Mohd, and M. Mazlin. 2009. Journal of Environmental Monitoring. 12: 287–295.

Imrie, C.E, Durucan, S. and Korea A. 2000. J.Hydrol. 233: 138–153.

Herman, I. 1994. Selangor: Tekno Edar–Descriptive statistical analysis

Aitchison, J. 1986. The Statistical Analysis of Compositional Data. Chapman & Hall, London, United Kingdom




DOI: http://dx.doi.org/10.11113/jt.v72.3013

Refbacks

  • There are currently no refbacks.


Copyright © 2012 Penerbit UTM Press, Universiti Teknologi Malaysia.
Disclaimer : This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.
Best viewed: Mozilla Firefox 4.0 & Google Chrome at 1024 × 768 resolution.