Semantic Kriging for Spatio-temporal Prediction / by Shrutilipi Bhattacharjee, Soumya Kanti Ghosh, Jia Chen

Von:

Bhattacharjee, Shrutilipi [VerfasserIn]

Mitwirkende(r):

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Studies in Computational Intelligence ; 839 | Springer eBooks Intelligent Technologies and RoboticsVerlag: Singapore : Springer, 2019Auflage: 1st ed. 2019Beschreibung: 1 Online-Ressource (XXV, 127 p. 92 illus., 76 illus. in color)ISBN:

9789811386640

Schlagwörter:

Andere physische Formen: 9789811386633 | Erscheint auch als: 978-981-13-8663-3 Druck-AusgabeDDC-Klassifikation:

006.3 23

LOC-Klassifikation:

Q342

DOI: DOI: 10.1007/978-981-13-8664-0Online-Ressourcen:

Zusammenfassung: 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 ResearchZusammenfassung: 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 terrainPPN: PPN: 1671010965Package identifier: Produktsigel: ZDB-2-INR | ZDB-2-SEB | ZDB-2-SXIT

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