03402nam a22005775i 4500 978-3-031-60946-6 DE-He213 20251030112638.0 cr nn 008mamaa 240720s2024 sz | s |||| 0|eng d 9783031609466 978-3-031-60946-6 10.1007/978-3-031-60946-6 doi GB651-2998 RBK bicssc SCI081000 bisacsh RBK thema Teegavarapu, Ramesh S.V. author. aut http://id.loc.gov/vocabulary/relators/aut 80910 Imputation Methods for Missing Hydrometeorological Data Estimation [electronic resource] / by Ramesh S.V. Teegavarapu. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XVII, 517 p. 292 illus., 22 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Water Science and Technology Library, 1872-4663 ; 108 Introduction 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. This 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. . Water. 65998 Hydrology. 65999 Statistics . 65982 Data mining. 6941 Engineering Data processing. Water. 65998 Statistics. 65982 Data Mining and Knowledge Discovery. Data Engineering. 66059 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031609459 Printed edition: 9783031609473 Printed edition: 9783031609480 Water Science and Technology Library, 1872-4663 ; 108 74491 https://doi.org/10.1007/978-3-031-60946-6 ZDB-2-EES ZDB-2-SXEE 61752 61752