Prediction of floodwater impacts on vehicle blockages at bridges using artificial neural network

¹ Department of Civil Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia
² Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500 Semenyih, Selangor, Malaysia

^* Corresponding author: Anurita.Selvarajoo@nottingham.edu.my
^* Corresponding author: Senthil.Arumugasamy@nottingham.edu.my

Abstract

During extreme flood events, various debris like floating vehicles can block the bridges in urban rivers and floodplains. Blockage of vehicles can influence the floodwater hydrodynamics and potentially on the flood risk implications. Such obstructions often raise upstream water levels with back water effects, causing more water to be redirected into nearby metropolitan areas. This study attempts at evaluating artificial neural network (ANN) model in predicting the variations in floodwater depths and velocities along the channel centreline based on the changes in flowrate and distances from the inlet. The floodwater depth and velocity variations were obtained for three different types of bridges at specified sites along the channel centreline with three incoming discharges. A multilayer feedforward neural network (FFNN) model was used to investigate the effects of discharge (Q) and distance, on depth variation rate (D) or velocity (V). Additionally, a comparison study was done between 2 input 1 output and 2 input 2 output i.e. single output (depth variation rate (D) or velocity (V) versus multi-output depth variation rate (D) and velocity (V) for all the three models of bridges that are blocked by vehicles. The study has applied 12 training algorithms (TA) in the ANN modelling to optimize the TA that is most suitable for the dataset of three different bridges. The optimization is based on the performance criterion namely regression (R), mean squared error (MSE), mean absolute error (MAE), mean absolute percentage (MAPE), accuracy and coefficient of determinant (R²). Bayesian regularization backpropagation (BR) training algorithm gives a highest accuracy when compared in all three bridges. The scenario 2 input 2 output gave greatest accuracy results compared to 2 input 1 output. The findings showed a reliable estimation of significant impacts on the flow propagations and the hydrodynamic processes along rivers and floodplains. This study can help the decision makers in effective river and floodplain management practices.