Problem of local scour depth around bridge piers is a critical issue that is to be considered for ensuring structural safety and mitigating risks associated with scouring. This study focuses on predicting local scour depth of unsteady flow under clear water condition using advanced machine learning methods including Adaptive Neuro-Fuzzy Inference System (ANFIS), Gene Expression Programming (GEP), and Artificial Neural Networks (ANN). A total of 353 input datasets were obtained from previous literature data and were divided in 70/30 ratio in which 70% (247) of datasets were used for training and 30% (106) of datasets were used for testing models. The performance of the developed models was evaluated using statistical indices such as Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Mean Absolute Percentage Error (MAPE). It was observed that ANN shows better results than GEP and ANFIS with RMSE of 0.05, R2 of 0.97, and MAPE of 12%. Thus, ANN can be used as an effective model for predicting scour depth of unsteady flow under clear water condition. This study contributes to advancing data-driven approaches for addressing challenges in hydraulic engineering.

Local scour, ANFIS, GEP, ANN