In:
Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 24, No. 9 ( 2020-09-17), p. 4523-4540
Abstract:
Abstract. Interpolation of spatial data has been regarded in many different forms, varying from deterministic to stochastic, parametric to nonparametric, and purely data-driven to geostatistical methods. In this study, we propose a nonparametric interpolator, which combines information theory with probability aggregation methods in a geostatistical framework for the stochastic estimation of unsampled points. Histogram via entropy reduction (HER) predicts conditional distributions based on empirical
probabilities, relaxing parameterizations and, therefore, avoiding the risk of adding information not present in data. By construction, it provides a proper framework for uncertainty estimation since it accounts for both
spatial configuration and data values, while allowing one to introduce or infer properties of the field through the aggregation method. We investigate the framework using synthetically generated data sets and demonstrate its efficacy in ascertaining the underlying field with varying sample densities and data properties. HER shows a comparable performance to popular benchmark models, with the additional advantage of higher generality. The novel method brings a new perspective of spatial interpolation and uncertainty analysis to geostatistics and statistical learning, using the lens of information theory.
Type of Medium:
Online Resource
ISSN:
1607-7938
DOI:
10.5194/hess-24-4523-2020
DOI:
10.5194/hess-24-4523-2020-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2020
detail.hit.zdb_id:
2100610-6
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