How a Computational Method Can Help to Improve the Quality of River Flood Prediction by Simulation

High performance computing has become a fundamental technology essential for computer simulation. Modelling and computational simulation provide powerful tools which enable flood event forecasting. In order to reduce flood damage, we have developed a methodology focused on enhancing a flood simulator minimizing the number of errors between simulated and observed results by using a two-phase optimization methodology via simulation. In this research, we implemented this approach to find the best solution or adjusted set of simulator input parameters. As a result of this, we achieved an improvement of up to 14 % which, for example, represents a significant difference of 0.5–1 m of water level along whole Parana River basin. In order to find the adjusted set of input parameters, we reduced the search space using a Monte Carlo + clustering K-Means method. Therefore, an exhaustive search over the reduced search space led us to get a “good solution”. In summary, we propose add an improvement process on the classical computer model output to improve model quality.