The objective is to develop applications and to integrate them with the most advanced computational technologies that can obtain flooding “real-time simulations”. These real-time simulations are ascertained by using efficient algorithms that make an adequate use of the computational resources (memory, caches, etc.) and are programmed to use the modern heterogeneous parallel computing systems (CPU+GPU). Another main goal is to allow this computational application to be remotely accessible using public cloud service, also known as “Cloud Computing”.
The final product will have the possibility of being applied to natural disasters related with water, such as simulations that are due to floods, tsunamis, storm surges, dam breaks, harbor resonance, etc. Usually, all of these scenarios generate a high social impact by provoking the loss of innumerable human lives and huge material damages with high economic costs and political impact.
The model used in FLOOTSI numerically resolves the Shallow Water Equations (SWE) and was implemented using an explicit scheme of high resolution Finite Volume Method (FVM). The chosen FVM scheme is of type “Riemann Solver of Roe” and admits dry and wet states for each pixel. The main advantage of this scheme is that its implementation on the GPU makes the application highly efficient. The combination of CPU and GPU allows the application to run simulations in real-time computing (RTC). The concept of “real-time” is used for simulations that run at the same speed as the real physical process without a significant delay.
The computational application uses as a basis for the simulation an image of a Digital Terrain Model (DTM) or a Digital Elevation Model (DEM). The latter not only includes the surface of the domain bottom but also all of the objects that are contained over that surface such as the vegetation or the buildings. The same pixels of the image conform the spatial discretization of the computational domain. This way, it prevents having to do a compute-intensive, time-consuming, hard to predict and prone to failure task of generating the usual meshes for the Finite Element Method (FEM).
These high resolution maps are usually ascertained from applying the remote sensing technology as is the laser altimetry too known as LIDAR mapping. These imagery are treated using technologies associated with Geographic Information Systems (GIS). For this reason, the model used for FLOOTSI counts with a highly versatile Graphical User Interface (GUI), based on the described technology. This user-friendly interface is used to manipulate the images, prepare and run the simulations. With the help of the same tool, one can swiftly visualize and analyze the results of the simulations.
All of these attributes define FLOOTSI as an agile and precise tool that can give valuable information about the flooding behavior before, during and after each event. In preparation for events some examples are: evaluate possible scenarios, creation of inundation maps, creation of emergency actions plans and evacuation, etc.
In other words, FLOOTSI can help to evaluate the different risks, palliate its effects and quantify the impact of the floods. This way, the generation of the possible flooding scenarios will allow specialists to make decisions based on useful compiled information from a combination of raw data, documents, and personal knowledge. All of the implementations have been verified and validated using real life events, allowing to conclude that FLOOTSI is an ideal Decision Support System (DSS) tool to evaluate efficiently the possible flooding risks.
Another valuable ability that FLOOTSI could count with is a Global Data Assimilation System
(GDAS) to transform it in an operational forecasting system. Data assimilation is a concept that encompasses any of the available methods for combining data received continuously in realtime from the Global Ocean Observing System (GODAS), satellites, terrestrial stations, ships, buoys, and other components into models used in numerical ocean prediction.
This tool will allow public and private organizations to get actionable, valuable intelligence from massive volumes of data and use predictive and prescriptive analytics to make better decisions and create strategical plans.
FLOOTSI will be an on-demand, “drag-and-drop” application that could offer users access to a range of public cloud service providers. This provides near-infinite “state-of-the-art” computing power to industries requiring real-time computations. This access will be attractive to SMEs but also to the existing high-volume users who often hit capacity issues, especially when deadlines are looming.
We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.