Design flood estimation for global river networks based on machine learning models
<p>Design flood estimation is a fundamental task in hydrology. In this research, we propose a machine-learning-based approach to estimate design floods globally. This approach involves three stages: (i) estimating at-site flood frequency curves for global gauging stations using the Anderson–Da...
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| Auteurs principaux: | , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Copernicus Publications
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/cc686fbb87c74b4680dbf28a08067fb8 |
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| Résumé: | <p>Design flood estimation is a fundamental task in
hydrology. In this research, we propose a machine-learning-based approach to
estimate design floods globally. This approach involves three stages: (i) estimating at-site flood frequency curves for global gauging stations using the
Anderson–Darling test and a Bayesian Markov chain Monte Carlo (MCMC) method; (ii) clustering these
stations into subgroups using a <span class="inline-formula"><i>K</i></span>-means model based on 12 globally
available catchment descriptors; and (iii) developing a regression model in
each subgroup for regional design flood estimation using the same
descriptors. A total of 11 793 stations globally were selected for model
development, and three widely used regression models were compared for design
flood estimation. The results showed that (1) the proposed approach
achieved the highest accuracy for design flood estimation when using all
12 descriptors for clustering; and the performance of the regression was
improved by considering more descriptors during training and validation; (2) a support vector machine regression provided the highest prediction
performance amongst all regression models tested, with a root mean square
normalised error of 0.708 for 100-year return period flood estimation; (3) 100-year design floods in tropical, arid, temperate, cold and polar climate
zones could be reliably estimated (i.e. <span class="inline-formula"><i><</i>±</span>25 % error), with
relative mean bias (RBIAS) values of <span class="inline-formula">−</span>0.199, <span class="inline-formula">−</span>0.233, <span class="inline-formula">−</span>0.169, 0.179 and <span class="inline-formula">−</span>0.091
respectively; (4) the machine-learning-based approach developed in this
paper showed considerable improvement over the index-flood-based method
introduced by Smith et al. (2015, <a href="https://doi.org/10.1002/2014WR015814">https://doi.org/10.1002/2014WR015814</a>) for
design flood estimation at global scales; and (5) the average RBIAS in
estimation is less than 18 % for 10-, 20-, 50- and 100-year design floods. We
conclude that the proposed approach is a valid method to estimate design
floods anywhere on the global river network, improving our prediction of the
flood hazard, especially in ungauged areas.</p> |
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