Calibration of Hydraulic Roughness Using Supervised Classification

Abstract

Flood prediction accuracy depends on properly calibrated hydraulic models and roughness parameters. This work proposes a machine learning -based framework trained on comparisons between reference and simulated solutions to predict error levels, evaluate Manning roughness configurations, and identify optimal parameterizations. The approach reduces computational costs while preserving hydraulic simulation reliability.