Experimental and Artificial Neural Network (ANN) Modeling of Instream Vegetation Hydrodynamic Resistance

Abstract

This study examines the impact of upstream structures on the bulk drag coefficient of vegetation through experimental means, which has not been previously conducted. An embankment model was placed upstream of the vegetation, both with and without a moat/depression. The results showed that the presence of an upstream structure reduced the bulk drag coefficient of vegetation as the structure shared the drag. When only the embankment was placed upstream, a maximum decrease of 11% in the bulk drag coefficient was observed. However, when both the embankment and moat models were placed upstream, a 20% decrease in the bulk drag coefficient was observed. Regression models and artificial neural network (ANN) models were developed to predict the bulk drag coefficient based on the variables affecting it. Five ANN models with different training functions were compared to find the best possible training function, with performance indicators such as coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), sum of square error (SSE), mean absolute error (MAE), and Taylor’s diagrams used to evaluate the model performance. The ANN model with nine neurons in each hidden layer performed the best, achieving the highest R2 and NSE values and the lowest RMSE, SSE, and MAE values. Finally, the comparison between the regression model and the ANN model showed that the best ANN model outperformed the regression models, achieving R2 values of 0.99 and 0.98 for the training and validation subsets, respectively.