rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Regionalization of Watersheds
    sub title: An Approach Based on Cluster Analysis
  name:
    name part: Rao, A. Ramachandra.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Srinivas, V.V.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: ne
    date issued: 2008
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 245 p. online resource.
  abstract: 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. .
  table of contents: Regionalization by Hybrid Cluster Analysis -- Regionalization by Fuzzy Cluster Analysis -- Regionalization by Artificial Neural Networks -- Effect of Regionalization on Flood Frequency Analysis -- Concluding Remarks.
  note: by A. Ramachandra Rao, V.V. Srinivas.
  subject:
    topic: Earth sciences
  subject:
    topic: Hydrology
  subject:
    topic: Hydrogeology
  subject:
    topic: Pattern recognition
  subject:
    topic: Regional planning
  subject:
    topic: Urban planning
  subject:
    topic: Statistics
  subject:
    topic: Civil engineering
  subject:
    topic: Earth Sciences
  subject:
    topic: Hydrogeology
  subject:
    topic: Hydrology/Water Resources
  subject:
    topic: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  subject:
    topic: Civil Engineering
  subject:
    topic: Pattern Recognition
  subject:
    topic: Landscape/Regional and Urban Planning
  classification: GB1001-1199.8
  classification: 551.4
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Water Science and Technology Library, 58
  identifier: 9781402068522
  identifier: http://dx.doi.org/10.1007/978-1-4020-6852-2
  record info:
    record creation date: 100301
    record change date: 20151204164224.0
    record identifier: 978-1-4020-6852-2