Flood management using 3-dimensional inundation map from gis spatial analyst; case study: Pekan sub-district - Ahmad, Zuhairi; Shammodin, Muhammad Shaheed; Abd. Hamid, Felza Zulhibri; Singh, Ranjit (2018)
A flood modelling study was carried out focusing on flood prone area, Pekan sub-district, Pahang. 3-dimensional inundation map model was used to simulate and predict flood affected zone within the area. The observation of inundation pattern mainly highlights the relativity between land topography with surface water level from Department of Irrigation and Drainage (DID) gauge station of Pahang River located at Pulau Jawa pump house. In addition, the coastal vegetation in Pahang River estuary particularly mangroves has direct effect on the erosion and deposition processes in the river. Such effects were observed to be greater during each flooding seasons. In relation to that, from previous studies, we have complete detail database of Pahang State mangroves except Pahang River Estuary and Cherating estuary mangrove. Thus, additional objective of mapping coastal vegetation in Pahang River estuary and Cherating estuary were included in this study. Methods involved of processing multiple types of spatial information gathered from remotely sensed data within a GIS environment. Data was collected from close and open source provider for the input during modelling process. A set of satellite imagery and digital elevation data was used to build a 3D spatial features of Pekan sub-district. Normal gauge reading of Pahang River was identified from DID infobanjir website at 1.0m and the hazard level increase at 2.44m, 3.05m, and finally 3.66m. Multiple water body layers were populated based on these data and flood pattern was observed until 5.50m gauge reading projection. 3D terrain model was created with assimilation of terrain information from digital elevation model. Water body offsets projection were generated and flood was observed in stereo mode within GIS software. Pekan sub-district was tesselated into square grid networks of 250m2 each. The technique allows identification of flood affected region in relation with water level offsets in simulation. Affected grids were assigned with different color codes for user recognition purpose. A set of feature class were created which represent important infrastructures especially main routes, community halls, hospital, and schools. This supplementary information could be used to assess the rate of habitability and accessibility during flood. Finally, DID historical datasets comparison together with on-site surveys were carried out to determine and validate the outcome of the simulated inundation model with real flood situation. For coastal vegetation mapping, unsupervised and supervised classification of NDVI Landsat 8 OLI satellite images were carried out. The area of degradation and expansion of coastal vegetation were then observed. This study was aimed to produce a flood prediction model that deliver interface that can be understood easily by any level of audience. It will help in earlier preparation especially for the locals if flood were to be discovered in the near future. Information provided on affected region and infrastructures that were emphasized will allow systematic planning and support flood relief committee in emergency period.