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Semantic kriging for spatio-temporal...

Bhattacharjee, Shrutilipi.

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Semantic kriging for spatio-temporal prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Semantic kriging for spatio-temporal prediction/ by Shrutilipi Bhattacharjee, Soumya Kanti Ghosh, Jia Chen.
Author:	Bhattacharjee, Shrutilipi.
other author:	Ghosh, Soumya Kanti.
Published:	Singapore :Springer Singapore : : 2019.,
Description:	xxv, 127 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Meteorology - Mathematical models. -
Online resource:	https://doi.org/10.1007/978-981-13-8664-0
ISBN:	9789811386640

Semantic kriging for spatio-temporal prediction
Bhattacharjee, Shrutilipi.

Semantic kriging for spatio-temporal prediction[electronic resource] /by Shrutilipi Bhattacharjee, Soumya Kanti Ghosh, Jia Chen. - Singapore :Springer Singapore :2019. - xxv, 127 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8391860-949X ;. - Studies in computational intelligence ;v.839..

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.

ISBN: 9789811386640

Standard No.: 10.1007/978-981-13-8664-0doiSubjects--Topical Terms:

566557
Meteorology
--Mathematical models.

LC Class. No.: QC866 / .B43 2019

Dewey Class. No.: 551.5028

Semantic kriging for spatio-temporal prediction
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100 1 $a Bhattacharjee, Shrutilipi. $3 3412277
245 1 0 $a Semantic kriging for spatio-temporal prediction $h [electronic resource] / $c by Shrutilipi Bhattacharjee, Soumya Kanti Ghosh, Jia Chen.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a xxv, 127 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.839
505 0 $a 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.
520 $a 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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650 0 $a Geospatial data $x Mathematical models. $3 1966164
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650 2 4 $a Remote Sensing/Photogrammetry. $3 890882
650 2 4 $a Artificial Intelligence. $3 769149
700 1 $a Ghosh, Soumya Kanti. $3 3412278
700 1 $a Chen, Jia. $3 1035899
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830 0 $a Studies in computational intelligence ; $v v.839. $3 3412279
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950 $a Intelligent Technologies and Robotics (Springer-42732)