000 04179nam a22005775i 4500
001 978-1-4020-6852-2
003 DE-He213
005 20161121230913.0
007 cr nn 008mamaa
008 100301s2008 ne | s |||| 0|eng d
020 _a9781402068522
_9978-1-4020-6852-2
024 7 _a10.1007/978-1-4020-6852-2
_2doi
050 4 _aGB1001-1199.8
072 7 _aRBK
_2bicssc
072 7 _aSCI081000
_2bisacsh
082 0 4 _a551.4
_223
100 1 _aRao, A. Ramachandra.
_eauthor.
245 1 0 _aRegionalization of Watersheds
_h[electronic resource] :
_bAn Approach Based on Cluster Analysis /
_cby A. Ramachandra Rao, V.V. Srinivas.
264 1 _aDordrecht :
_bSpringer Netherlands,
_c2008.
300 _aXI, 245 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aWater Science and Technology Library,
_x0921-092X ;
_v58
505 0 _aRegionalization by Hybrid Cluster Analysis -- Regionalization by Fuzzy Cluster Analysis -- Regionalization by Artificial Neural Networks -- Effect of Regionalization on Flood Frequency Analysis -- Concluding Remarks.
520 _aDesign 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. .
650 0 _aEarth sciences.
650 0 _aHydrology.
650 0 _aHydrogeology.
650 0 _aPattern recognition.
650 0 _aRegional planning.
650 0 _aUrban planning.
650 0 _aStatistics.
650 0 _aCivil engineering.
650 1 4 _aEarth Sciences.
650 2 4 _aHydrogeology.
650 2 4 _aHydrology/Water Resources.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aCivil Engineering.
650 2 4 _aPattern Recognition.
650 2 4 _aLandscape/Regional and Urban Planning.
700 1 _aSrinivas, V.V.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781402068515
830 0 _aWater Science and Technology Library,
_x0921-092X ;
_v58
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4020-6852-2
912 _aZDB-2-EES
950 _aEarth and Environmental Science (Springer-11646)
999 _c505689
_d505689