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HydroZIP: How Hydrological Knowledge can Be Used to Improve Compression of Hydrological Data

By Marc B. Parlange, Nick van de Giesen and Steven V. Weijs


From algorithmic information theory, which connects the information content of a data set to the shortest computer program that can produce it, it is known that there are strong analogies between compression, knowledge, inference and prediction. The more we know about a data generating process, the better we can predict and compress the data. A model that is inferred from data should ideally be a compact description of those data. In theory, this means that hydrological knowledge could be incorporated into compression algorithms to more efficiently compress hydrological data and to outperform general purpose compression algorithms. In this study, we develop such a hydrological data compressor, named HydroZIP, and test in practice whether it can outperform general purpose compression algorithms on hydrological data from 431 river basins from the Model Parameter Estimation Experiment (MOPEX) data set. HydroZIP compresses using temporal dependencies and parametric distributions. Resulting file sizes are interpreted as measures of information content, complexity and model adequacy. These results are discussed to illustrate points related to learning from data, overfitting and model complexity

Topics: data compression, algorithmic information theory, hydrology, inference, streamflow, MOPEX, Physics, QC1-999, Science, Q, DOAJ:Physics (General), DOAJ:Physics and Astronomy, Astrophysics, QB460-466
Publisher: MDPI AG
Year: 2013
DOI identifier: 10.3390/e15041289
OAI identifier:
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