Abstract
The spatial semantic kriging (SemK) based interpolation approach is an attempt to amalgamate semantic knowledge into the prediction process. It considers land-use/land-cover (LULC) information for the land–atmospheric interaction modeling to achieve better prediction outcome. However, the correlation study between every pair of LULC classes in SemK is a-priori, which is not a pragmatic approach. In this a-priori process, the influences of other nearby LULC classes is ignored in the interpolation process. This chapter establishes a modification of spatial SemK by extending this process with an a-posterior probability-based correlation analysis among different LULC classes. The fuzzy Bayesian network principle is utilized here to carry out the probabilistic analysis. The empirical evaluations with real land surface temperature data shows the need for probability-based correlation analysis in SemK by achieving more prediction accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beer M (2010) A summary on fuzzy probability theory. In: IEEE International conference on granular computing (GrC), IEEE, pp 5–6
Bhattacharjee S, Ghosh SK (2015) Performance evaluation of semantic kriging: a Euclidean vector analysis approach. IEEE Geosci Remote Sens Lett 12(6):1185–1189
Bhattacharjee S, Mitra P, Ghosh SK (2014) Spatial interpolation to predict missing attributes in GIS using semantic kriging. IEEE Trans Geosci Remote Sens 52(8):4771–4780
Bhattacharjee S, Das M, Ghosh SK, Shekhar S (2016) Prediction of meteorological parameters: an a-posteriori probabilistic semantic kriging approach. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems, ACM, p 38
Ferreira L, Borenstein D (2012) A fuzzy-Bayesian model for supplier selection. Expert Syst Appl 39(9):7834–7844
Li J (2008) A review of spatial interpolation methods for environmental scientists. Record. Geoscience Australia, Australia
Li PC, Chen GH, Dai LC, Zhang L (2012) A fuzzy Bayesian network approach to improve the quantification of organizational influences in HRA frameworks. Saf Sci 50(7):1569–1583
Penz CA, Flesch CA, Nassar SM, Flesch RC, De Oliveira MA (2012) Fuzzy-Bayesian network for refrigeration compressor performance prediction and test time reduction. Expert Syst Appl 39(4):4268–4273
Tang H, Liu S (2007) Basic theory of fuzzy Bayesian networks and its application in machinery fault diagnosis. In: Fourth international conference on fuzzy systems and knowledge discovery, vol 4. IEEE, pp 132–137
Wilcox A, Hripcsak G (1999) Classification algorithms applied to narrative reports. In: Proceedings of the AMIA symposium, American Medical Informatics Association, p 455
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Bhattacharjee, S., Ghosh, S.K., Chen, J. (2019). Fuzzy Bayesian Semantic Kriging. In: Semantic Kriging for Spatio-temporal Prediction. Studies in Computational Intelligence, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-8664-0_4
Download citation
DOI: https://doi.org/10.1007/978-981-13-8664-0_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-8663-3
Online ISBN: 978-981-13-8664-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)