國立虎尾科技大學 |

Semantic kriging for spatio-temporal prediction

Record Type:	Language materials, printed : 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.;
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. 50. .

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:

784053
Meteorology
--Mathematical models.

LC Class. No.: QC866 / .B43 2019

Dewey Class. No.: 551.5028

Semantic kriging for spatio-temporal prediction
LDR:02299nam a2200337 a 4500 001 941223
003 DE-He213
005 20190701131702.0
006 m d
007 cr nn 008maaau
008 200417s2019 si s 0 eng d
020 $a 9789811386640 $q (electronic bk.)
020 $a 9789811386633 $q (paper)
024 7 $a 10.1007/978-981-13-8664-0 $2 doi
035 $a 978-981-13-8664-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QC866 $b .B43 2019
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 551.5028 $2 23
090 $a QC866 $b .B575 2019
100 1 $a Bhattacharjee, Shrutilipi. $3 1228349
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 : $c 2019. $b Springer Singapore : $b Imprint: Springer,
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.
650 0 $a Meteorology $x Mathematical models. $3 784053
650 0 $a Geospatial data $x Mathematical models. $3 965517
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Remote Sensing/Photogrammetry. $3 670396
650 2 4 $a Artificial Intelligence. $3 646849
700 1 $a Ghosh, Soumya Kanti. $3 1228350
700 1 $a Chen, Jia. $3 815847
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Studies in computational intelligence ; $v v. 50. $3 720500 $3 770436
856 4 0 $u https://doi.org/10.1007/978-981-13-8664-0
950 $a Intelligent Technologies and Robotics (Springer-42732)