Semantic kriging for spatio-temporal prediction / by Shrutilipi Bhattacharjee, Soumya Kanti Ghosh, Jia Chen
By: Bhattacharjee, Shrutilipi, autor
Contributor(s): Ghosh, Soumya Kanti, autor | Chen, Jia, autor
Series: (Studies in Computational Intelligence, 1860-949X ; 839); (Intelligent Technologies and Robotics (Springer-42732)).Publisher: Singapore : Springer Singapore : Imprint: Springer, 2019Edition: First edition.Description: 1 recurso en línea (XXV, 127 páginas) : 92 ilustraciones, 76 ilustraciones a color.ISBN: 9789811386640.Subject: Meteorología -- Predicción | Meteorología -- Modelos matemáticosOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | QC866 2019 EB (Browse shelf(Opens below)) | Acceso electrónico | eBook07112115 |
Chapter 1. Introduction -- Chapter 2. Spatial Interpolation -- Chapter 3. Spatial Semantic Kriging -- Chapter 4. Fuzzy Bayesian Semantic Kriging -- Chapter 5. Spatio-temporal Reverse Semantic Kriging -- Chapter 6. Summary and Future Research.
This book identifies the need for modeling auxiliary knowledge of the terrain to enhance the prediction accuracy of meteorological parameters. The spatial and spatio-temporal prediction of these parameters are important for the scientific community, and the semantic kriging (SemK) and its variants facilitate different types of prediction and forecasting, such as spatial and spatio-temporal, a-priori and a-posterior, univariate and multivariate. As such, the book also covers the process of deriving the meteorological parameters from raw satellite remote sensing imagery, and helps understanding different prediction method categories and the relation between spatial interpolation methods and other prediction methods. The book is a valuable resource for researchers working in the area of prediction of meteorological parameters, semantic analysis (ontology-based reasoning) of the terrain, and improving predictions using auxiliary knowledge of the terrain.
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