2013
DOI: 10.1080/02626667.2013.822640
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An entropy approach for the optimization of cross-section spacing for river modelling

Abstract: An accurate definition of river geometry is essential to implement one-dimensional (1D) hydraulic models and, in particular, appropriate spacing between cross-sections is key for capturing a river's hydraulic behaviour. This work explores the potential of an entropy-based approach, as a complementary method to existing guidelines, to determine the optimal number of cross-sections to support 1D hydraulic modelling. To this end, given a redundant collection of existing cross-sections, a location subset is select… Show more

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Cited by 34 publications
(31 citation statements)
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References 53 publications
(51 reference statements)
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“…In particular, there is a limit in the number of CSD for which satisfactory model improvements can be achieved and for which additional CSD become redundant. This asymptotic behaviour, when extra information is added, has also been observed using other metrics by Krstanovic and Singh (1992), Ridolfi et al (2014), Alfonso et al (2013), among others. From Fig.…”
Section: Discussionsupporting
confidence: 73%
“…In particular, there is a limit in the number of CSD for which satisfactory model improvements can be achieved and for which additional CSD become redundant. This asymptotic behaviour, when extra information is added, has also been observed using other metrics by Krstanovic and Singh (1992), Ridolfi et al (2014), Alfonso et al (2013), among others. From Fig.…”
Section: Discussionsupporting
confidence: 73%
“…This means that the additional CS observations do not add information useful for improving the model performance. This asymptotic behavior when extra information is added has also been observed using other metrics by Krstanovic and Singh (1992), Ridolfi et al (2014), Alfonso et al (2013), among others. In all flood events, similar trends of the NSE are found.…”
Section: Experiments 51supporting
confidence: 73%
“…Although the approach described so far has been applied in different studies (see e.g., [29][30][31]34]), the estimation of the Information Theory quantities presented in Section 2.1 is sensitive to different assumptions in the calculation of probabilities. An example is the histogram bin size used to estimate probabilities via frequency methods.…”
Section: Ensemble Entropy In Monitoring Network Designmentioning
confidence: 99%