National Center for High Performance Computing, NARLabs
National Center for High-performance Computing (NCHC), founded in 1991, is Taiwan's only national-level supercomputing center. The NCHC plays a leading role in cloud technology that possesses a large computing, storage, networking and platform integration, providing the domestic users with the Could Integration Service of High Performance Computing (HPC), high quality networking, high efficiency storage, big data analysis and scientific engineering simulation.
The goal of the NCHC is to become an internationally renowned HPC center that promotes scientific discovery and technological innovation. Since its inception, the NCHC has been dedicated to strengthening Taiwan's HPC and networking infrastructure. The NCHC has planned and implemented pilot research programs in HPC, cloud computing, as well as big data processing methods and applications. The NCHC provides professional technologies and platform services to academia, government, and industry, and helps to cultivate domestic talent in HPC-related fields.
In order to effectively support Taiwan's technology research, the NCHC constructed technology R&D platforms to support domestic and foreign R&D teams in developing HPC and big data applications, which cover engineering and science, environmental and disaster prevention, biomedicine, and digital cultural content creation, aiming to become a first-rate High-performance Computing Center.
Database of rainfall-induced inundation simulations applied in the development of AI models for flooding forecast
Solution Description
We intend to present a rainfall-inundation simulation database which comprises a big number of simulated rainfall-induced inundations by collaborating the storm generator with 2D hydraulic numerical model successfully adapted in NCHC TWCC platform. In detail, the storm generator mainly reproduces gridded rainfall data with high resolution in time and space (i.e. quantitative precipitation estimaton, QPE) by means of Monte Carlo simulation method for the non-normal correlated multi-variates with the gridded rainfall characteristics under the consideration of correlation structure in time and space. After that, the simulated gridded rainstorms are imported into the 2D hydraulic numerical model (SOBEK) to carry out the inundation simulation. Eventually, a big number of rainfall-induced simulations can be obtained so as to create a databased regarding the rainfall-inundation simulation. Accordingly, through TWCC platform, the resulting rainfall-inundation-simulation database can be applied in training a variety of AI models for the flooding forecasts, including the artificial neural network (ANN), the long short-term memory (LSTM), convolutional Neural Network (CNN) and support vector machine (SVM) as shown in the webpage. The relevant results from training AI models using the proposed rainfall-inundation simulations database indicate that the aforementioned AI models are well-established ones which can provide information on the flooding forecasts.