We use models to simulate the real world mainly for prediction purposes. However,
since any model is a simplification of reality, there remains a great deal of
uncertainty even after the calibration of model parameters. The model’s identifiability
of realistic model parameters becomes questionable when the watershed of interest
is small, and its time of concentration is shorter than the computational time step of
the model. To improve the discovery of more reliable and more realistic sets of model
parameters instead of mathematical solutions, a new algorithm is needed. This algorithm
should be able to identify mathematically inferior but more robust solutions as
well as to take samples uniformly from high-dimensional search spaces for the purpose
of uncertainty analysis.
Various watershed configurations were considered to test the Soil and Water Assessment
Tool (SWAT) model’s identifiability of the realistic spatial distribution of
land use, soil type, and precipitation data. The spatial variability in small watersheds
did not significantly affect the hydrographs at the watershed outlet, and the SWAT
model was not able to identify more realistic sets of spatial data. A new populationbased
heuristic called the Isolated Speciation-based Particle Swarm Optimization
(ISPSO) was developed to enhance the explorability and the uniformity of samples in
high-dimensional problems. The algorithm was tested on seven mathematical functions
and outperformed other similar algorithms in terms of computational cost, consistency,
and scalability. One of the test functions was the Griewank function, whose number of minima is not well defined although the function serves as the basis for
evaluating multi-modal optimization algorithms. Numerical and analytical methods
were proposed to count the exact number of minima of the Griewank function within
a hyperrectangle. The ISPSO algorithm was applied to the SWAT model to evaluate
the performance consistency of optimal solutions and perform uncertainty analysis
in the Generalized Likelihood Uncertainty Estimation (GLUE) framework without
assuming a statistical structure of modeling errors. The algorithm successfully found
hundreds of acceptable sets of model parameters, which were used to estimate their
prediction limits. The uncertainty bounds of this approach were comparable to those
of the typical GLUE approach.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2920 |
| Date | 15 May 2009 |
| Creators | Cho, Huidae |
| Contributors | Olivera, Francisco |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
Page generated in 0.0021 seconds