Efficacy of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System Models in Scour Depth Prediction at Submerged Weirs

Accurate assessment of scour depth is crucial for the safe design of hydraulic structures, particularly around submerged weirs, where the scouring process is inherently complex due to intricate sediment transport and scour mechanisms. Although several studies have addressed scour at submerged weirs, the literature reveals limited investigations. Traditionally, scour depth prediction models have relied on regression analysis of laboratory data. However, recent advancements in soft computing techniques have shown superior predictive capabilities for complex modeling problems. This study uses soft computing models, specifically Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), to predict scour depth at submerged weirs. These models are trained and validated using existing experimental data from the literature. Our analysis demonstrates that ANN and ANFIS outperform conventional regression models in predicting scour depth. The conventional Feed-Forward Backpropagation (FFBP) neural network yielded the highest predictive accuracy among the soft computing approaches. Additionally, sensitivity analysis identified flow intensity and relative weir height as the most influential parameters affecting relative scour depth. These findings underscore the potential of soft computing techniques in enhancing the reliability of scour depth predictions for hydraulic design applications.