globalchange  > 气候变化与战略
DOI: 10.5194/hess-24-4523-2020
论文题名:
Histogram via entropy reduction (HER): An information-theoretic alternative for geostatistics
作者: Thiesen S.; Vieira D.M.; Mälicke M.; Loritz R.; Florian Wellmann J.; Ehret U.
刊名: Hydrology and Earth System Sciences
ISSN: 1027-5606
出版年: 2020
卷: 24, 期:9
起始页码: 4523
结束页码: 4540
语种: 英语
Scopus关键词: Benchmarking ; Entropy ; Graphic methods ; Interpolation ; Probability distributions ; Statistical methods ; Stochastic systems ; Uncertainty analysis ; Conditional distribution ; Empirical probabilities ; Geostatistical method ; Spatial configuration ; Spatial interpolation ; Statistical learning ; Stochastic estimation ; Uncertainty estimation ; Information theory ; benchmarking ; empirical analysis ; estimation method ; geostatistics ; histogram ; interpolation ; parameterization ; probability ; spatial analysis ; uncertainty analysis
英文摘要: 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. © 2020 Copernicus GmbH. All rights reserved.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/162595
Appears in Collections:气候变化与战略

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作者单位: Thiesen, S., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany; Vieira, D.M., Department for Microsystems Engineering, University of Freiburg, Freiburg, Germany, Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany; Mälicke, M., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany; Loritz, R., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany; Florian Wellmann, J., Computational Geosciences and Reservoir Engineering, RWTH Aachen University, Aachen, Germany; Ehret, U., Institute of Water Resources and River Basin Management, Karlsruhe Institute of Technology, Karlsruhe, Germany

Recommended Citation:
Thiesen S.,Vieira D.M.,Mälicke M.,et al. Histogram via entropy reduction (HER): An information-theoretic alternative for geostatistics[J]. Hydrology and Earth System Sciences,2020-01-01,24(9)
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