Abstract:
Flooding due to dam-break, excessive rainfall, and storm surge is a serious threatening hazard that can cause major disasters and economic losses. Also, there are significant chances of increase in the flooding and river inundation due to climate change. Various 1D and 2D hydraulic models have been developed to simulate these flood events. Shallow water equations are widely used around the world to simulate the flood models. But these flood modeling and forecasting using hydraulic models require much efficiency and computational time particularly for high resolution meshes. On account of this even nowadays most of the large-scale flood simulations run on the supercomputers. Various approaches have been used to overcome this big issue some of them are simplifying the numerical equations and parallelization. Parallelization is a technique in which versions of hydraulic model codes are developed that run in parallel on multiple cores. These algorithms usually based on multiple techniques such as message passing interface (MPI) that uses cores with distributed memory system, multiprocessor approach with shared memory system such as Open multiple processing (OpenMP) and graphic processor units (GPU) with massive parallel ability. These parallel approaches are now widely used to simulate the floods and have much beneficial results in order to simulate large scale floods with less time and less computational costs. In this study we have validated a model with real time world case and accelerate the computational efficiency by using parallel GPU based code.
