Fast Rainfall runoff simulation and parameter optimization
Typ
Examensarbete för masterexamen
Program
Complex adaptive systems (MPCAS), MSc
Publicerad
2020
Författare
Hesslow, Daniel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
Infiltration , Runoff , Bayesian Optimization , Parameter Calibration