| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910874670603321 |
|
|
Autore |
Teegavarapu Ramesh S. V |
|
|
Titolo |
Imputation Methods for Missing Hydrometeorological Data Estimation / / by Ramesh S.V. Teegavarapu |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2024 |
|
|
|
|
|
|
|
|
|
ISBN |
|
9783031609466 |
9783031609459 |
|
|
|
|
|
|
|
|
Edizione |
[1st ed. 2024.] |
|
|
|
|
|
Descrizione fisica |
|
1 online resource (532 pages) |
|
|
|
|
|
|
Collana |
|
Water Science and Technology Library, , 1872-4663 ; ; 108 |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Water |
Hydrology |
Statistics |
Data mining |
Engineering - Data processing |
Data Mining and Knowledge Discovery |
Data Engineering |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
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. |
|
|
|
|
|
|
|
|
Sommario/riassunto |
|
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. . |
|
|
|
|
|
| |