CWEEE  Vol.3 No.3 , July 2014
Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin
ABSTRACT

This study constructs downscaling statistical model in analyzing the hydrological modeling in the study area which faces the risk of flood occurrence as the impact of climate change. The combination of chemometric method and time series analysis in this study show that even during the monsoon season, rainfall and stream flow are not the major contribution towards the changing of water level in the study area. Based on Correlation Test, it shows that suspended solid and water level show high correlation with p-value < 0.05. Factor Analysis being carried out to determine the major contribution to the changes of Water Level and the result show that Suspended Solid shows a strong factor pattern with value 0.829. Based on Control Chat Builder for time series analysis, the Upper Control Limit for water level and suspended solid are 7.529 m and 1947.049 tons/day and the Lower Control Limit are 6.678 m and 178.135 tons/day. This shows that human development in the area gives high impact towards climate change and risk of flood in the study area which commonly faces flood during monsoon season.


Cite this paper
Mohd Saudi, A. , Juahir, H. , Azid, A. , Amri Kamarudin, M. , Toriman, M. and Abdul Aziz, N. (2014) Flood Risk Pattern Recognition Using Chemometric Technique: A Case Study in Muda River Basin. Computational Water, Energy, and Environmental Engineering, 3, 102-110. doi: 10.4236/cweee.2014.33011.
References
[1]   Moore, D.S. and McCabe, G.P. (1989) Introduction to the Practice of Statistics. W. H. Freeman, New York.

[2]   Altman, D.G. (1991) Practical Statistics for Medical Research. Chapman & Hall, London, 285-288.

[3]   Floyd, F.J. and Widaman, K.F. (1995) Factor Analysis in the Development and Refinement of Clinical Assessment Instruments. Psychological Assessment, 7, 286-299. http://dx.doi.org/10.1037/1040-3590.7.3.286

[4]   Gorsuch, R.L. (1990) Common Factor-Analysis versus Component Analysis: Some Well and Little Known Facts. Multivariate Behavioral Research, 25, 33-39.
http://dx.doi.org/10.1207/s15327906mbr2501_3

[5]   Thompson, B. and Daniel, L.G. (1996) Factor Analytic Evidence for the Construct Validity of Scores: A Historical Overview and Some Guidelines. Educational and Psychological Measurement, 56, 197-208.
http://dx.doi.org/10.1177/0013164496056002001

[6]   William, B., Brown, T. and Onsman, A. (2012) Exploratory Factor Analysis: A Five-Step Guide for Novices. Australasian Journal of Paramedicine, 8.

[7]   Trubin, I.A. (2008) Exception Based Modelling and Forecasting. Proceedings of the Computer Measurement Group, Nevada, 7-12 December 2008, 353-364.

[8]   Juahir, H., Sharifuddin, M.Z., Ahmad, Z.A, Mohd, K.Y. and Mazlin, M. (2009) Spatial Assessment of Langat RIVER Water Quality Using Chemometrics. Journal of Environmental Monitoring, 12, 287-295.
http://dx.doi.org/10.1039/b907306j

[9]   Imrie, C.E., Durucan, S. and Korea A. (2000) River Flow Prediction by Using Artificial Neural Networks: Generalisation beyond Calibration Range. Journal of Hydrology, 233,138-153.
http://dx.doi.org/10.1016/S0022-1694(00)00228-6

[10]   Saudi, A.S.M., Juahir, H., Azid, A., Yusof, K.M.K.K., Zainuddinc, S.F.M. and Osman, M.R. (2014) Spatial Assessment of Water Quality Due to Land-Use Changes along Kuantan River Basin. From Sources to Solution 2014, 297-300.

 
 
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