According to past studies, the simulation of runoff events with high hydraulic risk has posed many challenges for policymakers, environmentalists and engineers around the world. Using 1-D modelling to predict flood risk from different return period events or multiple land use and climate change scenarios are common(Horritt & Bates, 2002).
It is noticeable that the use of the Digital Elevation Model (DEM) in the creation of flood models have reached an important role of the topographic and hydrological analysis of basin data, since it represents a series of elevations in the basin at regularly spaced intervals. This removes the assumption that the basin or area is a flat surface without contours(Heimhuber et al., 2015).
In a research conducted by Sarawut Jamrussri and Yuji Toda on the hydraulic models and GIS for the study of the Mae Klong River in Thailand. Flow frequency analysis was used in the creation of a flood risk map. The study also showed that the simulation results were correctly presented in GIS and DTM format, using contour and height data from the river point. Sarawut Jamrussri and Yuji Toda conclude their study by suggesting that more studies be done in large basins, dividing them into sub-basins and introducing the network link to integrate them to have a general view of the basin. Runoff from floodplains, fluvial canals and artificial structures are important factors in the study of the prediction of runoff flow patterns, the researchers added. rainwater in upstream areas and not stable(Jamrussri & Toda, 2017).
In case study on flood risk and flood prediction using GIS and the model of hydrodynamic presented the possibility of using DEM controlled in a GIS and translated into MIKE21. In the study, different scenarios were checked out, and results were translated into the GIS environment for flood visualization and analysis during a 100-year flood return period. However, Jagadish Prasad Patraa, Rakesh Kumara and Pankaj Manib pointed out that there was no real way to calibrate the simulations from the modelling output, as flood and stage data for the floods were rarely recorded and compared between the MIKE21 and MIKE1 results, the first being an improvement of the last one(Prasad, Kumar, & Mani, 2016).