Commit e2920ba0 authored by Delaigue Olivier's avatar Delaigue Olivier
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refactor(Publications): fix a doi link in the 'Articles' section

Refs #2
parent ab885645
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### Upcoming
- Althoff D., Rodrigues L.N. & Bazame H.C. (2021). Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment. doi: [https://doi.org/10.1007/s00477-021-01980-8](https://doi.org/10.1007/s00477-021-01980-8)
- Althoff D., Rodrigues L.N. & Bazame H.C. (2021). Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble. Stochastic Environmental Research and Risk Assessment. doi: [10.1007/s00477-021-01980-8](https://doi.org/10.1007/s00477-021-01980-8)
- Astagneau P.C., Thirel G., Delaigue O., Guillaume J.H.A., Parajka J., Brauer C.C., et al. (2020). Hydrology modelling R packages: a unified analysis of models and practicalities from a user perspective. Hydrology and Earth System Sciences Discussions 2020, 1–48. doi: [10.5194/hess-2020-498](https://doi.org/10.5194/hess-2020-498)
- Ayzel G., Kurochkina L., Abramov D. & Zhuravlev S. (2021). Development of a Regional Gridded Runoff Dataset Using Long Short-Term Memory (LSTM) Networks. Hydrology 8, 6. doi: [10.3390/hydrology8010006](https://doi.org/10.3390/hydrology8010006)
- Ayzel G., Kurochkina L. & Zhuravlev S. (2020). The influence of regional hydrometric data incorporation on the accuracy of gridded reconstruction of monthly runoff. Hydrological Sciences Journal, 1–12. doi: [10.1080/02626667.2020.1762886](https://doi.org/10.1080/02626667.2020.1762886)
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