Prediction of meteorological parameters: An a-posteriori probabilistic semantic kriging approach

Shrutilipi Bhattacharjee, Monidipa Das, Soumya K. Ghosh, Shashi Shekhar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Meteorological parameters are often considered as crucial factors for climatological pattern analysis. Predictions of these parameters have been studied extensively in the field of remote sensing and GIS. It is one of the most critical steps involved in most of the meteorological data mining process. Spatial interpolation is an eficient technique to yield minimal error in prediction. From existing literatures, it is evident that the land-use/land-cover (LULC) distribution of the terrain inuences these parameters in a varying manner and it is important to model their behaviour for climatological analyses. However, this semantic LULC knowledge of the terrain is generally ignored in the prediction process of the meteorological parameters. Recently, we have proposed a new spatial interpolation technique, namely semantic kriging (SemK) [3, 5, 7], which considers the semantic LULC knowledge for land-atmospheric interaction modeling and incorporates it into the existing interpolation process for better accuracy. However, the a-priori correlation analysis of SemK ignores the effect of other nearby LULC classes on each other. This article presents a new variant of SemK, namely a-posterior probabilistic Bayesian SemK (BSemK), which extends the a-priori correlation analysis of SemK with a-posterior probabilistic analysis. The proposed approach provides more accurate estimation of the parameters. Experimentation with LST data advocates the effcacy of the proposed approach compared to the a-priori SemK and other existing interpolation techniques.

Original languageEnglish (US)
Title of host publication24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
EditorsMatthias Renz, Mohamed Ali, Shawn Newsam, Matthias Renz, Siva Ravada, Goce Trajcevski
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450345897
DOIs
StatePublished - Oct 31 2016
Event24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 - Burlingame, United States
Duration: Oct 31 2016Nov 3 2016

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
Volume0

Other

Other24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
Country/TerritoryUnited States
CityBurlingame
Period10/31/1611/3/16

Bibliographical note

Publisher Copyright:
© 2016 ACM.

Keywords

  • Bayesian analysis
  • Land-atmospheric interaction
  • Meteorological parameters
  • Prediction
  • Semantic Kriging
  • Spatial interpolation

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