Spatiotemporal Interpolation for Environmental Modelling
AbstractA variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications. View Full-Text
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Susanto, F.; de Souza, P., Jr.; He, J. Spatiotemporal Interpolation for Environmental Modelling. Sensors 2016, 16, 1245.
Susanto F, de Souza P, Jr, He J. Spatiotemporal Interpolation for Environmental Modelling. Sensors. 2016; 16(8):1245.Chicago/Turabian Style
Susanto, Ferry; de Souza, Paulo, Jr.; He, Jing. 2016. "Spatiotemporal Interpolation for Environmental Modelling." Sensors 16, no. 8: 1245.
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