A Bayesian calibration method for urban flood models introduces latent variables and adjoint equations to tune parameters like Manning's coefficient, achieving relative errors from 1.16% to 13.88% on test cases.
Case 1 simulates a square area consisting of a total of 30*30 cells, with a cell edge length of 10m as depicted in Fig.2
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Calibration of the underlying surface parameters for urban flood using latent variables and adjoint equation
A Bayesian calibration method for urban flood models introduces latent variables and adjoint equations to tune parameters like Manning's coefficient, achieving relative errors from 1.16% to 13.88% on test cases.