Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-28464Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ulzega, Simone | - |
| dc.contributor.author | Albert, Carlo | - |
| dc.date.accessioned | 2023-08-18T12:33:43Z | - |
| dc.date.available | 2023-08-18T12:33:43Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.issn | 1027-5606 | de_CH |
| dc.identifier.issn | 1607-7938 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/28464 | - |
| dc.description.abstract | Stochastic models in hydrology are very useful and widespread tools for making reliable probabilistic predictions. However, such models are only accurate at making predictions if model parameters are first of all calibrated to measured data in a consistent framework such as the Bayesian one, in which knowledge about model parameters is described through probability distributions. Unfortunately, Bayesian parameter calibration, a. k. a. inference, with stochastic models, is often a computationally intractable problem with traditional inference algorithms, such as the Metropolis algorithm, due to the expensive likelihood functions. Therefore, the prohibitive computational cost is often overcome by employing over-simplified error models, which leads to biased parameter estimates and unreliable predictions. However, thanks to recent advancements in algorithms and computing power, fully fledged Bayesian inference with stochastic models is no longer off-limits for hydrological applications. Our goal in this work is to demonstrate that a computationally efficient Hamiltonian Monte Carlo algorithm with a timescale separation makes Bayesian parameter inference with stochastic models feasible. Hydrology can potentially take great advantage of this powerful data-driven inference method as a sound calibration of model parameters is essential for making robust probabilistic predictions, which can certainly be useful in planning and policy-making. We demonstrate the Hamiltonian Monte Carlo approach by detailing a case study from urban hydrology. Discussing specific hydrological models or systems is outside the scope of our present work and will be the focus of further studies. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Copernicus | de_CH |
| dc.relation.ispartof | Hydrology and Earth System Sciences | de_CH |
| dc.rights | https://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | Bayesian inference | de_CH |
| dc.subject | Stochastic model | de_CH |
| dc.subject | Hamiltonian Monte Carlo | de_CH |
| dc.subject | Environmental sciences | de_CH |
| dc.subject.ddc | 510: Mathematik | de_CH |
| dc.subject.ddc | 551: Geologie und Hydrologie | de_CH |
| dc.title | Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | Life Sciences und Facility Management | de_CH |
| zhaw.organisationalunit | Institut für Computational Life Sciences (ICLS) | de_CH |
| dc.identifier.doi | 10.5194/hess-27-2935-2023 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-28464 | - |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 15 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.pages.end | 2950 | de_CH |
| zhaw.pages.start | 2935 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 27 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.funding.snf | 169295 | de_CH |
| zhaw.webfeed | Biomedical Simulation | de_CH |
| zhaw.webfeed | High Performance Computing (HPC) | de_CH |
| zhaw.funding.zhaw | BISTOM - Bayesian Inference with Stochastic Models | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| zhaw.relation.references | https://github.com/ulzegasi/HMC_SIP | de_CH |
| Appears in collections: | Publikationen Life Sciences und Facility Management | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2023_Ulzega-Albert_Bayesian-parameter-inference-in-hydrological-modelling_HESS.pdf | 5.32 MB | Adobe PDF |  View/Open |
Show simple item record
Ulzega, S., & Albert, C. (2023). Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model. Hydrology and Earth System Sciences, 27(15), 2935–2950. https://doi.org/10.5194/hess-27-2935-2023
Ulzega, S. and Albert, C. (2023) ‘Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model’, Hydrology and Earth System Sciences, 27(15), pp. 2935–2950. Available at: https://doi.org/10.5194/hess-27-2935-2023.
S. Ulzega and C. Albert, “Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model,” Hydrology and Earth System Sciences, vol. 27, no. 15, pp. 2935–2950, 2023, doi: 10.5194/hess-27-2935-2023.
ULZEGA, Simone und Carlo ALBERT, 2023. Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model. Hydrology and Earth System Sciences. 2023. Bd. 27, Nr. 15, S. 2935–2950. DOI 10.5194/hess-27-2935-2023
Ulzega, Simone, and Carlo Albert. 2023. “Bayesian Parameter Inference in Hydrological Modelling Using a Hamiltonian Monte Carlo Approach with a Stochastic Rain Model.” Hydrology and Earth System Sciences 27 (15): 2935–50. https://doi.org/10.5194/hess-27-2935-2023.
Ulzega, Simone, and Carlo Albert. “Bayesian Parameter Inference in Hydrological Modelling Using a Hamiltonian Monte Carlo Approach with a Stochastic Rain Model.” Hydrology and Earth System Sciences, vol. 27, no. 15, 2023, pp. 2935–50, https://doi.org/10.5194/hess-27-2935-2023.
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