Frequency Analysis of Droughts Using Stochastic and Soft Computing Techniques
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Date
2010-05-19T20:29:50Z
Authors
Sadri, Sara
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
In the Canadian Prairies recurring droughts are one of the realities which can
have significant economical, environmental, and social impacts. For example,
droughts in 1997 and 2001 cost over $100 million on different sectors. Drought frequency
analysis is a technique for analyzing how frequently a drought event of a given
magnitude may be expected to occur. In this study the state of the science related
to frequency analysis of droughts is reviewed and studied. The main contributions
of this thesis include development of a model in Matlab which uses the qualities of
Fuzzy C-Means (FCMs) clustering and corrects the formed regions to meet the criteria
of effective hydrological regions. In FCM each site has a degree of membership in
each of the clusters. The algorithm developed is flexible to get number of regions and
return period as inputs and show the final corrected clusters as output for most case
scenarios. While drought is considered a bivariate phenomena with two statistical
variables of duration and severity to be analyzed simultaneously, an important step
in this study is increasing the complexity of the initial model in Matlab to correct
regions based on L-comoments statistics (as apposed to L-moments). Implementing
a reasonably straightforward approach for bivariate drought frequency analysis using
bivariate L-comoments and copula is another contribution of this study. Quantile estimation at ungauged sites for return periods of interest is studied by introducing two
new classes of neural network and machine learning: Radial Basis Function (RBF)
and Support Vector Machine Regression (SVM-R). These two techniques are selected
based on their good reviews in literature in function estimation and nonparametric
regression. The functionalities of RBF and SVM-R are compared with traditional
nonlinear regression (NLR) method. As well, a nonlinear regression with regionalization
method in which catchments are first regionalized using FCMs is applied and
its results are compared with the other three models. Drought data from 36 natural
catchments in the Canadian Prairies are used in this study. This study provides a
methodology for bivariate drought frequency analysis that can be practiced in any
part of the world.
Description
Keywords
Droughts, Frequency Analysis, Fuzzy C-Means, Clustering, Regionalization, L-moments, L-comoments, Bivariate Copula, Radial Basis Function, Support Vector Machine Regression, Nonlinear Regression, Nonlinear Regression with Regionalization