Fast Rainfall runoff simulation and parameter optimization
Examensarbete för masterexamen
Complex adaptive systems (MPCAS), MSc
Since the number of flooding events are expected to rise in the coming years as a consequence of global warming, accurate simulation of such events are now more important than ever. Running simulations and seeing the effects of different possible actions serves as a very important tool to mitigate the consequences of such events. For the results of the simulations to be accurate it is important that both the parameters that govern the surface flow and the subsurface flow are known, or that they can be accurately estimated. While it is feasible to measure some parameters to sufficient accuracy, such as the topology, this is not true for for all parameters. The subsurface flow is governed by the soil characteristics at all points in the simulation space and may vary over the depth. Additionally, measuring the soil characteristics at any one point is expensive. It is, therefore, not feasible to measure the soil characteristics at all points and all depths to a sufficient accuracy. The traditional approach is to have an expert estimate all such parameters, however this is costly and if ground truth data from previous flooding events are available the parameters can instead be tuned to fit with the previous events. In this thesis an efficient and numerically accurate way to calculate the infiltration of the multi-layer Green-Ampt model is presented as well as a method for automatically optimizing the parameters of large-scale fluid simulations. The developed methods are implemented in the VISDOM-application developed by VRVIS and evaluated on different scenarios.
Infiltration , Runoff , Bayesian Optimization , Parameter Calibration