Abstract
Flood simulations are vital to gain insight into possible dangers and damages for effective emergency planning. With flexible and natural ways of visualizing water flow, more precise evaluation of the study area is achieved. In this study, we describe a method for flood visualization using both regular and adaptive grids for position-based fluids method to visualize the depth of water in the study area. The mapping engine utilizes adaptive cell sizes to represent the study area and utilizes Jenks natural breaks method to classify the data. Predefined single-hue and multi-hue color sets are used to generate a heat map of the study area. It is shown that the dynamic representation benefits the mapping engine through enhanced precision when the study area has non-disperse clusters. Moreover, it is shown that, through decreasing precision, and utilizing an adaptive grid approach, the simulation runs more efficiently when particle interaction is computationally expensive.
Highlights
Throughout history, floods have been an integral part of our lives
Our mapping engine, which has the power to decide the method of representation considering environmental parameters and computational expense, provides an efficient and optimized approach to visualize water flow for flood analysis
Our visualization approach provides a framework for mapping the depth of any 2D or 3D Lagrangian, particle-based water flow simulation, which is important for flooding scenarios
Summary
Throughout history, floods have been an integral part of our lives. Floods are major disasters that have been responsible for the loss of millions of lives and the collapse of cities. Since the introduction of SPH by Gingold and Monaghan [6] and Lucy [9], it has been used in a variety of industries, including computer graphics It is a mesh-free particle-based method based on Lagrangian formulation. In our work, considering state-of-the-art approaches discussed, we utilized position-based fluids [7] for simulating flood, which consider a Lagrangian description and provide certain benefits over traditional SPH [6] methods. Our mapping engine, which has the power to decide the method of representation considering environmental parameters and computational expense, provides an efficient and optimized approach to visualize water flow for flood analysis. Our visualization approach provides a framework for mapping the depth of any 2D or 3D Lagrangian, particle-based water flow simulation, which is important for flooding scenarios.
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