Randomly Supported Variations of Deterministic Models and Their Application to One-Dimensional Shallow Water Flows
Publication: Journal of Hydraulic Engineering
Volume 150, Issue 5
Abstract
This paper deals with the prediction of flows in open channels. For this purpose, models based on partial differential equations are used. Such models require the estimation of constitutive parameters based on available data. After this estimation, the solution of the equations produces predictions of flux evolution. In this work, we consider that most natural channels may not be well represented by deterministic models for many reasons. Therefore, we propose to estimate parameters using stochastic variations of the original models. There are two types of parameters to be estimated: constitutive parameters (such as roughness coefficients) and the parameters that define the stochastic variations. Both types of estimates will be computed using the maximum likelihood principle, which determines the objective function to be used. After obtaining the parameter estimates, due to the random nature of the stochastic models, we are able to make probabilistic predictions of the flow at times or places where no observations are available.
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Data Availability Statement
All data, models, and code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by FAPESP (Grant Nos. 2013/07375-0, 2018/24293-0, and 2022/05803-3) and CNPq (Grant Nos. 304192/2019-8, 302538/2019-4, and 302682/2019-8).
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Published In
Journal of Hydraulic Engineering
Volume 150 • Issue 5 • September 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Apr 19, 2023
Accepted: Mar 19, 2024
Published online: Jun 13, 2024
Published in print: Sep 1, 2024
Discussion open until: Nov 13, 2024
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