This thesis presents a new approach to calibration of hydrologic
models using distributed computing framework. Distributed hydrologic
models are known to be very computationally intensive and difficult
to calibrate. To cope with the high computational cost of the
process a Surrogate Model Optimization (SMO) technique that is built
for distributed computing facilities is proposed. The proposed
method along with two analogous SMO methods are employed to
calibrate WATCLASS hydrologic model. This model has been developed
in University of Waterloo and is now a part of Environment Canada
MESH (Environment Canada community environmental modeling system
called Modèlisation Environmentale Communautaire (MEC) for
Surface Hydrology (SH)) systems.
SMO has the advantage of being less sensitive to "curse of
dimensionality" and very efficient for large scale and
computationally expensive models. In this technique, a mathematical
model is constructed based on a small set of simulated data from the
original expensive model. SMO technique follows an iterative
strategy which in each iteration the surrogate model map the region
of optimum more precisely.
A new comprehensive method based on a smooth regression model is
proposed for calibration of WATCLASS. This method has at least two
advantages over the previously proposed methods: a)it does not
require a large number of training data, b) it does not have many
model parameters and therefore its construction and validation
process is not demanding.
To evaluate the performance of
the proposed SMO method, it has been applied to five well-known test
functions and the results are compared to two other analogous SMO
methods. Since the performance of all SMOs are promising, two
instances of WATCLASS modeling Smoky River watershed are calibrated
using these three adopted SMOs and the resultant Nash-Sutcliffe
numbers are reported.
| Identifer | oai:union.ndltd.org:WATERLOO/oai:uwspace.uwaterloo.ca:10012/4287 |
| Date | 17 February 2009 |
| Creators | Kamali, Mahtab |
| Source Sets | University of Waterloo Electronic Theses Repository |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
Page generated in 0.011 seconds