Regression on Deep Visual Features using Artificial Neural Networks (ANNs) to Predict Hydraulic Blockage at Culverts

TL;DR

This paper develops a machine learning pipeline that uses deep visual features from culvert images to predict hydraulic blockage, aiming to improve flood risk assessment and infrastructure maintenance.

Contribution

It introduces an end-to-end regression approach combining deep learning and physical models to quantify hydraulic blockage from visual data, addressing gaps in current policy-based methods.

Findings

ANN with MobileNet features achieved R² of 0.7855.

Deep visual features correlate with hydraulic blockage.

Pipeline performance varies with models and hardware.

Abstract

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.