This study focuses on optimizing the use of high-performance computing on public cloud infrastructure, along with information theory, for assessing water systems. These assessments are computationally intensive and can benefit from parallel computing and the evaluation of the collected data with information theory. A case study of a water system analysis for the Vlietpolder was conducted to test various cloud configuration settings using an embarrassingly parallel batch computation. The hydrodynamic simulations involved D-HYDRO 1D2D models with different precipitation events and model resolutions. The modelling results were quantified using normalized Shannon’s Entropy to facilitate the comparison of system configurations, evaluating the batch computation process to determine whether enough simulations have been performed and comparing individual simulations.

The study showed that public cloud infrastructure provides comparable computational performance to local computers and servers, and offers opportunities for vertical and horizontal scaling for parallelization. The study also provides insight into the impact of allocated resources, node size, and node type on cloud infrastructure performance. Furthermore, the quantified information derived from the simulations can be utilized to evaluate the batch
computation output and support cost-benefit analyses for selecting configuration settings and model decisions given modeling scenarios.

The study concludes that combining cloud infrastructure and information theory can enhance hydrodynamic modelling for batch computations in water system analysis. The findings provide insights into the potential benefits of utilizing public cloud infrastructure for large-scale computations of hydrodynamic simulations.