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020 _a9783031609466
_9978-3-031-60946-6
024 7 _a10.1007/978-3-031-60946-6
_2doi
050 4 _aGB651-2998
072 7 _aRBK
_2bicssc
072 7 _aSCI081000
_2bisacsh
072 7 _aRBK
_2thema
100 1 _aTeegavarapu, Ramesh S.V.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980910
245 1 0 _aImputation Methods for Missing Hydrometeorological Data Estimation
_h[electronic resource] /
_cby Ramesh S.V. Teegavarapu.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXVII, 517 p. 292 illus., 22 illus. in color.
_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,
_x1872-4663 ;
_v108
505 0 _aIntroduction to Missing Data -- Methods for Imputation of Missing Data -- Temporal Interpolation Methods -- Spatial Interpolation Methods -- Data Driven Modes for Imputation -- Multiple Imputation Methods -- Evaluation of Methods and Imputed Data.
520 _aThis book serves as a unique reference that provides a comprehensive discussion about the state-of-the-art spatial and temporal, statistical, and data mining methods for imputation of missing hydrometeorological data. The primary audience for this book are researchers, scientists, graduate-level and higher students, modelers, data scientists working in hydrological, meteorological, climatological, and earth sciences. Data analysts in other disciplines will also be interested in this book. The book appeals to graduate students, researchers in civil and environmental engineering, geosciences, climatology, and hydrological and data sciences. It will be an ideal reference book for state and federal agencies that collect and process hydrometeorological and climatological data. Engineering professionals, hydrologists, meteorologists, and data and spatial analysts dealing with large hydrometeorological datasets. Researchers and modelers involve in the development of long-term datasets at national and international institutions. .
650 0 _aWater.
_965998
650 0 _aHydrology.
_965999
650 0 _aStatistics .
_965982
650 0 _aData mining.
_96941
650 0 _aEngineering
_xData processing.
650 1 4 _aWater.
_965998
650 2 4 _aStatistics.
_965982
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aData Engineering.
_966059
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031609459
776 0 8 _iPrinted edition:
_z9783031609473
776 0 8 _iPrinted edition:
_z9783031609480
830 0 _aWater Science and Technology Library,
_x1872-4663 ;
_v108
_974491
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60946-6
912 _aZDB-2-EES
912 _aZDB-2-SXEE
999 _c61752
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