Malaysian Journal of Analytical Sciences Vol 19 No 5 (2015): 991 - 1000

 

 

 

FLOOD RISK INDEX ASSESSMENT IN JOHOR RIVER BASIN

 

(Penilaian Indeks Risiko Banjir di Lembangan Sungai Johor)

 

Ahmad Shakir Mohd Saudi^1,2, Hafizan Juahir¹*, Azman Azid¹, Fazureen Azaman¹

 

¹East Coast Environmental Research Institute (ESERI),

Universiti Sultan Zainal Abidin, Gong Badak Campus,21300 Kuala Terengganu, Terengganu, Malaysia

² Faculty of Science and Technology,

Open University Malaysia, 40100 Shah Alam, Selangor Darul Ehsan, Malaysia

 

*Corresponding author: hafizanjuahir@unisza.edu.my

 

 

Received: 14 April 2015; Accepted: 9 July 2015

 

 

Abstract

This study is focusing on constructing the flood risk index in the Johor river basin. The application of statistical methods such as factor analysis (FA), statistical process control (SPC) and artificial neural network (ANN) had revealed the most efficient flood risk index. The result in FA was water level has correlation coefficient of 0.738 and the most practicable variable to be used for the warning alert system. The upper control limits (UCL) for the water level in the river basin Johor is 4.423m and the risk index for the water level has been set by this method consisting of 0-100.The accuracy of prediction has been evaluated by using ANN and the accuracy of the test result was R² = 0.96408 with RMSE= 2.5736. The future prediction for UCL in Johor river basin has been predicted and the value was 3.75m. This model can shows the current and future prediction for flood risk index in the Johor river basin and can help local authorities for flood control and prevention of the state of Johor.

 

Keywords:  Flood Risk Index, Johor River Basin, factor analysis, upper control limit, future prediction

 

Abstrak

Kajian ini memberi tumpuan kepada pembinaan indeks risiko banjir di lembangan sungai Johor. Penggunaan kaedah statistik seperti analisis faktor (FA), kawalan proses statistik (SPC) dan rangkaian neural buatan (ANN) telah mendedahkan indeks risiko banjir yang paling berkesan. Hasil dalam FA menunjukkan bahawa paras air mempunyai pekali korelasi 0.738 dan pembolehubah yang paling praktikal untuk digunakan sebagai satu sistem amaran. Had kawalan atas (UCL) bagi paras air di lembangan sungai Johor adalah 4.423m dan juga indeks risiko untuk paras air telah dibentuk melalui kaedah ini yang terdiri daripada 0-100. Ketepatan ramalan telah dinilai dengan menggunakan ANN dan ketepatan keputusan ujian adalah R² = 0.96408 dengan RMSE = 2.5736. Ramalan masa depan untuk UCL di lembangan sungai Johor telah diramalkan dan nilai tersebut adalah 3.75m. Model ini dapat menunjukkan ramalan semasa dan masa depan untuk indeks risiko banjir di lembangan sungai Johor dengan cekap dan dapat membantu Pihak Berkuasa Tempatan untuk kawalan banjir dan pencegahan negeri Johor.

 

Kata kunci:  Indeks Risiko Banjir, Lembangan Sungai Johor, analisis faktor, kawalan had tinggi, ramalan masa depan

 

References

1.        Strahler, N and Strahler, A (1978). Geography and man’s environment, John Wiley and Sons, U.S.A.

2.        Ward, R. C. and Smith, K. (1998). Flooding: Physical processes and human impacts, John Wiley and Jones Ltd, England.

3.        Nicholls, J.R.,  Natasha, M., Jason A. L., Sally B., Pier V., Diogo D.G., Jochen H. and Richard S. J. (2010). Sea-level rise and its possible impacts given a ‘beyond 4°C world’ in the twenty-first century. Journal of the Royal Society of Philosophical Transaction 369: 161-181.

4.        Mouhsen, S., Zulkifli, Y., Fadhilah, Y., Shamsuddin, S., and Milad J.  (2013). Flood Frequency Analysis Based on the t-copula for Johor River, Malaysia. Journal of Applied Sciences 13: 1021-1028.

5.        Gasim M. B., Surif S., Mokhtar M., Toriman M. E., Rahim S. A., and Chong H. B. (2010). Flood Analysis of December 2006: Focus at Segamat Town, Johor. Sains Malaysiana 39: 353–361.

6.        Floyd, F. J. and Widaman, K. F. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment 7(3), 286-299.

7.        Gorsuch, R. L. (1990). Common Factor-Analysis versus Component Analysis - Some Well and Little Known Facts. Multivariate Behavioral Research 25(1), 33-39.

8.        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(2):197-208.

9.        William, B., Brown, T. and Onsman, A. (2010). Exploratory factor analysis: A five-step guide for novices. Australasian Journal of Paramedicine 8(3): 1-13.

10.     Trubin, I. (2008). Exception Based Modeling and Forecasting. Proceedings of the Computer Measurement Group.

11.     Imrie, C.E, Durucan, S. and Korea A. (2000). River flow prediction by using Artificial Neural Networks: generalisation beyond calibration range. Journal Hydrology 233:138-153.

12.     Wong, W. K., Manzur, M. and Chew, B. K. (2003). How rewarding is technical analysis? Evidence from Singapore stock market. Journal of Applied Financial Economics 13:543-551.

13.     Saudi, A. S. M., Juahir, H., Azid, A., Yusof, K. M. K., Zainuddin, S. F. M., Osman, M. R. (2014). Spatial Assessment of Water Quality Due To Land-Use Changes along Kuantan River Basin. From Sources to Solution 2014: pp 297-300.

14.     Li, X., Guo, S., Liu, P., and Chen, G. (2010). Dynamic control of flood limited water level for reservoir operation by considering inflow uncertainty. Journal of Hydrology 391:124–132.

15.     Mouhsen, S., Zulkifli, Y., Fadhilah, Y., Shamsuddin, S., and Milad J.  (2013). Flood Frequency Analysis Based on the t-copula for Johor River, Malaysia. Journal of Applied Sciences 13: 1021-1028.