  Hydraulic blockage of cross-drainage structures such as culverts is
considered one of main contributor in triggering urban flash floods. However,
due to lack of during floods data and highly non-linear nature of debris
interaction, conventional modelling for hydraulic blockage is not possible.
This paper proposes to use machine learning regression analysis for the
prediction of hydraulic blockage. Relevant data has been collected by
performing a scaled in-lab study and replicating different blockage scenarios.
From the regression analysis, Artificial Neural Network (ANN) was reported best
in hydraulic blockage prediction with $R^2$ of 0.89. With deployment of
hydraulic sensors in smart cities, and availability of Big Data, regression
analysis may prove helpful in addressing the blockage detection problem which
is difficult to counter using conventional experimental and hydrological
approaches.
