Please use this identifier to cite or link to this item:
Title: Application of Stochastic Flood Forecasting Model Using Regression Method for Kelantan Catchment
Authors: Osman, S. 
Aziz, N.A. 
Husaif, N. 
Sidek, L.M. 
Shakirah, A. 
Hanum, F. 
Basri, H. 
Issue Date: 2018
Abstract: Flood is without doubt the most devastating natural disasters, striking numerous regions in Malaysia each year. During the last decades, the trend in flood damages has been growing exponentially. This is a consequence of the increasing frequency of heavy rain, changes in upstream land-use and a continuously increasing concentration of population and assets in flood prone areas. Malaysia, periodically, have faced with huge floods since previous years. Kelantan River basin, which located in the Northeast of Peninsular Malaysia, is prone to flood events in Malaysia. Kelantan River is the principal cause of flooding because it is constricted at its lower reaches. The capacity of the river at the downstream coastal area is less than 10,000 m3/s, therefore flood that exceeds this capacity will overspill the banks and discharge overland to the sea. Realizing the seriousness of the problems, it is vital in providing in time useful information for making crucial decisions especially to provide warning for any potential flood occurrence. In this study, stochastic flood forecasting model using stage regression method was applied to Kelantan River basin, in which the regression coefficients and equations was derived from the least square principle. The stochastic model were calibrated and validated which then shows that the equations derived are suitable to predict the hydrograph in Kelantan River basin. In conclusion, establishing a flood forecasting system would enhance the effectiveness of all other mitigation measures by providing time for appropriate actions. This has increased the importance of flood modelling for flood forecasts to issue advance warning in severe storm situations to reduce loss of lives and property damage. © The Authors, published by EDP Sciences, 2018.
DOI: 10.1051/matecconf/201820307001
Appears in Collections:UNITEN Scholarly Publication

Show full item record

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.