Abstract
This monograph focuses on the prediction and forecasting of the meteorological parameters that are related to the earth surface. These parameters are mainly derived from the raster satellite imagery, and generally contain missing and erroneous pixels, line gaps, and cloud covers. These issues are considered as the major hindrances to generate complete raster surface for these parameters. In this situation, the spatial interpolation methods are reported to be the most efficient choice in many literature. This monograph attempts to incorporate the LULC-based contextual knowledge of the terrain for the interpolation process of the meteorological parameters.
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Reference
Bhattacharjee S (2016) Semantic kriging: a semantically enhanced approach for spatial interpolation. PhD thesis, Indian Institute of Technology (IIT) Kharagpur, India
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© 2019 Springer Nature Singapore Pte Ltd.
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Bhattacharjee, S., Ghosh, S.K., Chen, J. (2019). Summary and Future Research. 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_6
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DOI: https://doi.org/10.1007/978-981-13-8664-0_6
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Online ISBN: 978-981-13-8664-0
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