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

Entropy

Steven V. Weijs¹  Nick van de Giesen² 

[1] School of Architecture, Civil and Environmental Engineering, EPFL, Station 2, Lausanne 1015, Switzerland;Water Resources Management, TU Delft, Stevinweg 1, Delft 2628 CN, The Netherlands

关键词: data compression;    algorithmic information theory;    hydrology;    inference;    streamflow;    MOPEX;   

DOI  :  10.3390/e15041289

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.