Design of water control structures, reservoir management,
economic evaluation of flood protection projects, land use planning
and management, flood insurance assessment, and other projects rely
on knowledge of magnitude and frequency of floods. Often,
estimation of floods is not easy because of lack of flood records
at the target sites. Regional flood frequency analysis (RFFA)
alleviates this problem by utilizing flood records pooled from
other watersheds, which are similar to the watershed of the target
site in flood characteristics.
Clustering techniques are used to identify group(s) of
watersheds which have similar flood characteristics. This book is a
comprehensive reference on how to use these techniques for RFFA and
is the first of its kind. It provides a detailed account of several
recently developed clustering techniques, including those based on
fuzzy set theory and artificial neural networks. It also documents
research findings on application of clustering techniques to RFFA
that remain scattered in various hydrology and water resources
journals.
The optimal number of groups defined in an area is based on
cluster validation measures and L-moment based homogeneity tests.
These form the bases to check the regions for homogeneity.
The subjectivity involved and the effort needed to identify
homogeneous groups of watersheds with conventional approaches are
greatly reduced by using efficient clustering techniques discussed
in this book. Furthermore, better flood estimates with smaller
confidence intervals are obtained by analysis of data from
homogeneous watersheds. Consequently, the problem of over- or
under-designing by using these flood estimates is reduced. This
leads to optimal economic design of structures. The advantages of
better regionalization of watersheds and their utility are entering
into hydrologic practice.
Audience
This book will be of interest to researchers in stochastic
hydrology, practitioners in hydrology and graduate students.
General
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