Abstract:Calibrating the urban underlying surface parameters is crucial for urban flood simulation. We formulate the parameter calibration problem into an optimization problem within the Bayesian framework using the maximum likelihood principle. We adopt the urban flood dynamical system model as the surrogate model and innovatively introduce latent variables inspired by machine learning to represent more uncertainties, which can also be compatible with common physical parameter calibration. For more efficient optimization, we construct the adjoint equation of the surrogate model to obtain gradient information and propose the parameter sharing technique and the localization technique to reduce the computation complexity of the adjoint equation. A simple case verifies the proposed method can converge quickly and is insensitive to the observation time interval. In the case derived from Test 8A, we calibrate Manning's coefficient of urban roads, with a maximum relative error of 13.88% and a minimum of 1.16%.
| Comments: | 27 pages, 8 figures, 2 table, submitted to Journal of Flood Risk Management |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.02959 [cs.LG] |
| (or arXiv:2605.02959v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.02959 arXiv-issued DOI via DataCite |
From: Yongfu Tian [view email]
[v1]
Sun, 3 May 2026 01:59:38 UTC (1,108 KB)
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