Prediction of Hydraulic Blockage at Cross Drainage Structures using Regression Analysis

TL;DR

This paper explores machine learning regression, especially ANN, to predict hydraulic blockage in cross-drainage structures, addressing data scarcity and non-linearity issues with lab data and potential smart city sensor deployment.

Contribution

It introduces a regression-based approach, particularly using ANN, for hydraulic blockage prediction, overcoming limitations of traditional models.

Findings

ANN achieved an R^2 of 0.89 in blockage prediction

Lab data was used to train the regression models

Regression analysis can aid in smart city sensor deployment for blockage detection

Abstract

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.