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Imputation methods for missing hydro...

Teegavarapu, Ramesh S. V.

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Imputation methods for missing hydrometeorological data estimation

Record Type:	Electronic resources : Monograph/item
Title/Author:	Imputation methods for missing hydrometeorological data estimation/ by Ramesh S.V. Teegavarapu.
Author:	Teegavarapu, Ramesh S. V.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xvii, 517 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Statistical matching. -
Online resource:	https://doi.org/10.1007/978-3-031-60946-6
ISBN:	9783031609466

Imputation methods for missing hydrometeorological data estimation
Teegavarapu, Ramesh S. V.

Imputation methods for missing hydrometeorological data estimation[electronic resource] /by Ramesh S.V. Teegavarapu. - Cham :Springer International Publishing :2024. - xvii, 517 p. :ill. (some col.), digital ;24 cm. - Water science and technology library,v. 1081872-4663 ;. - Water science and technology library ;v. 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.

ISBN: 9783031609466

Standard No.: 10.1007/978-3-031-60946-6doiSubjects--Topical Terms:

740906
Statistical matching.

LC Class. No.: GB2801.72.S7

Dewey Class. No.: 551.57

Imputation methods for missing hydrometeorological data estimation
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300 $a xvii, 517 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Water science and technology library, $x 1872-4663 ; $v v. 108
505 0 $a 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.
520 $a 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.
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