TY  - BOOK
AU  - Teegavarapu,Ramesh S.V.
ED  - SpringerLink (Online service)
TI  - Imputation Methods for Missing Hydrometeorological Data Estimation
T2  - Water Science and Technology Library,
SN  - 9783031609466
AV  - GB651-2998 
PY  - 2024///
CY  - Cham
PB  - Springer International Publishing, Imprint: Springer
KW  - Water
KW  - Hydrology
KW  - Statistics 
KW  - Data mining
KW  - Engineering
KW  - Data processing
KW  - Statistics
KW  - Data Mining and Knowledge Discovery
KW  - Data Engineering
N1  - 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
N2  - 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.
UR  - https://doi.org/10.1007/978-3-031-60946-6
ER  -