東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Imputation methods for missing hydro...

Teegavarapu, Ramesh S. V.

FindBook

Google Book

Amazon

博客來

Imputation methods for missing hydrometeorological data estimation

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Imputation methods for missing hydrometeorological data estimation/ by Ramesh S.V. Teegavarapu.
作者:	Teegavarapu, Ramesh S. V.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xvii, 517 p. :ill. (some col.), digital ;24 cm.
內容註:	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
標題:	Statistical matching. -
電子資源:	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
LDR:02378nmm a2200337 a 4500 001 2374009
003 DE-He213
005 20240720125228.0
006 m d
007 cr nn 008maaau
008 241231s2024 sz s 0 eng d
020 $a 9783031609466 $q (electronic bk.)
020 $a 9783031609459 $q (paper)
024 7 $a 10.1007/978-3-031-60946-6 $2 doi
035 $a 978-3-031-60946-6
040 $a GP $c GP
041 0 $a eng
050 4 $a GB2801.72.S7
072 7 $a RBK $2 bicssc
072 7 $a SCI081000 $2 bisacsh
072 7 $a RBK $2 thema
082 0 4 $a 551.57 $2 23
090 $a GB2801.72.S7 $b T258 2024
100 1 $a Teegavarapu, Ramesh S. V. $3 3722560
245 1 0 $a Imputation methods for missing hydrometeorological data estimation $h [electronic resource] / $c by Ramesh S.V. Teegavarapu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
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.
650 0 $a Statistical matching. $3 740906
650 0 $a Hydrometeorology $x Statistical methods. $3 3463038
650 0 $a Mathematical statistics. $3 516858
650 1 4 $a Water. $3 516619
650 2 4 $a Statistics. $3 517247
650 2 4 $a Data Mining and Knowledge Discovery. $3 898250
650 2 4 $a Data Engineering. $3 3409361
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Water science and technology library ; $v v. 108. $3 3722561
856 4 0 $u https://doi.org/10.1007/978-3-031-60946-6
950 $a Earth and Environmental Science (SpringerNature-11646)