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https://doi.org/10.21256/zhaw-28464| Publication type: | Article in scientific journal |
| Type of review: | Peer review (publication) |
| Title: | Bayesian parameter inference in hydrological modelling using a Hamiltonian Monte Carlo approach with a stochastic rain model |
| Authors: | Ulzega, Simone Albert, Carlo |
| et. al: | No |
| DOI: | 10.5194/hess-27-2935-2023 10.21256/zhaw-28464 |
| Published in: | Hydrology and Earth System Sciences |
| Volume(Issue): | 27 |
| Issue: | 15 |
| Page(s): | 2935 |
| Pages to: | 2950 |
| Issue Date: | 2023 |
| Publisher / Ed. Institution: | Copernicus |
| ISSN: | 1027-5606 1607-7938 |
| Language: | English |
| Subjects: | Bayesian inference; Stochastic model; Hamiltonian Monte Carlo; Environmental sciences |
| Subject (DDC): | 510: Mathematics 551: Geology and hydrology |
| 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. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/28464 |
| Related research data: | https://github.com/ulzegasi/HMC_SIP |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 4.0: Attribution 4.0 International |
| Departement: | Life Sciences and Facility Management |
| Organisational Unit: | Institute of Computational Life Sciences (ICLS) |
| Published as part of the ZHAW project: | BISTOM - Bayesian Inference with Stochastic Models |
| 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 |
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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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