  Cross drainage hydraulic structures (i.e., culverts, bridges) in urban
landscapes are prone to getting blocked by transported debris which often
results in causing the flash floods. In context of Australia, Wollongong City
Council (WCC) blockage conduit policy is the only formal guideline to consider
blockage in design process. However, many argue that this policy is based on
the post floods visual inspections and hence can not be considered accurate
representation of hydraulic blockage. As a result of this on-going debate,
visual blockage and hydraulic blockage are considered two distinct terms with
no established quantifiable relation among both. This paper attempts to relate
both terms by proposing the use of deep visual features for prediction of
hydraulic blockage at a given culvert. An end-to-end machine learning pipeline
is propounded which takes an image of culvert as input, extract visual features
using deep learning models, pre-process the visual features and feed into
regression model to predict the corresponding hydraulic blockage. Dataset
(i.e., Hydrology-Lab Dataset (HD), Visual Hydrology-Lab Dataset (VHD)) used in
this research was collected from in-lab experiments carried out using scaled
physical models of culverts where multiple blockage scenarios were replicated
at scale. Performance of regression models was assessed using standard
evaluation metrics. Furthermore, performance of overall machine learning
pipeline was assessed in terms of processing times for relative comparison of
models and hardware requirement analysis. From the results ANN used with
MobileNet extracted visual features achieved the best regression performance
with $R^{2}$ score of 0.7855. Positive value of $R^{2}$ score indicated the
presence of correlation between visual features and hydraulic blockage and
suggested that both can be interrelated with each other.
