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Flood risk analysis in Sg. Sam, Kuala Krai, Kelantan using remote sensing and GIS technique

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Published under licence by IOP Publishing Ltd
, , Citation Wani Sofia Udin and Naimah Abd Malek 2018 IOP Conf. Ser.: Earth Environ. Sci. 169 012060 DOI 10.1088/1755-1315/169/1/012060

1755-1315/169/1/012060

Abstract

In Malaysia, flood hazard is commonly occurred due to the geographical characteristic of the country. It is caused by an abundance of rains during the monsoon seasons and convection rains during the hot but humid periods. There is high demand in current trend and future scenarios of flood risks for accurate spatial and temporal information on the potential hazards and risks of flood especially in Kelantan as the state has been affected by floods annually. The research was conducted at Sungai Sam, Kuala Krai, Kelantan and the study area covers about 25 km². The objectives of the research are to produce flood risk area map covers during the worst flood event occurred on December 2014 of Sg. Sam, Kuala Krai, Kelantan and to correlate flood risk area map with land cover changes. In order to achieve objectives, the integrated of spatial technology of remote sensing and Geographical Information System (GIS) were used as tools. As a result, the flood risk area map consists of particular level has been successfully produced based on a few parameters such as accuracy of Digital Elevation Model (DEM), water level, flood depth and amounts of rainfall. Landsat images of pre-flooding and post-flooding were obtained to analyse the flood occurrence. Flood risk analysis is made by correlating flood risk area (low, medium and high) with land cover changes. The flood risk map produced clearly shows the spatial distribution of the flooded area which is located at areas with relatively low relief. The result showed that the significant decrease of plantation area has occurred in high and medium risk zone while land use at low risk zone was affected by urbanization process. Thus, integration of GIS coupled with terrain model and remotely sensed data can produce flood risk map with good accuracy.

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