Prediction of Hydraulic Blockage at Cross Drainage Structures using Regression Analysis
This addresses the blockage detection problem in urban flood management, which is difficult with conventional methods, but it is incremental as it applies existing regression techniques to a specific domain.
The paper tackles the problem of predicting hydraulic blockage at cross-drainage structures, which contributes to urban flash floods, by using machine learning regression analysis on scaled in-lab data, achieving an R^2 of 0.89 with an Artificial Neural Network.
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.