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Scouring of streambed is\u00a0a significant problem and ultimately results in the failure of the bridge\u00a0when the abutments are exposed to flowing water over the long term. Abutment scour is influenced by the type of abutment, shape, and size of the abutments. In the current study, machine learning (ML) models have been utilized for predicting the scour depth around abutments making use of experimental data. The scour depth was modeled around three types of abutments: a vertical wall, a semicircular wall, and a 45\u00b0 wing wall. Five input parameters, namely, the length of the abutment (L), breadth of the abutment (B), sediment size (d<jats:sub>50<\/jats:sub>), approaching flow depth (h) and average approaching flow velocity (U), were used in this study. For predicting the abutment scour depth, ML models such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Gradient Tree Boosting (GTB), Group Method of Data Handling (GMDH), and Multivariate Adaptive Regression Splines (MARS) were applied. Statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative RMSE (RRMSE), Normalized Nash\u2013Sutcliffe Efficiency (NNSE), Kling-Gupta Efficiency (KGE), and Willmott Index (WI) have been employed to evaluate the performance of each model. It was found that the GTB model provided relatively accurate\u00a0predictions of\u00a0the scour depth around the semicircular and 45\u00b0 wing wall abutments with good metrics. Similarly, the MARS model outperformed all other models in terms of predicting vertical wall abutment scour depth.<\/jats:p>","DOI":"10.1007\/s00521-023-09109-4","type":"journal-article","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T14:02:21Z","timestamp":1699624941000},"page":"1369-1380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Prediction of scour depth around bridge abutments using ensemble machine learning models"],"prefix":"10.1007","volume":"36","author":[{"given":"Sreedhara B.","family":"Marulasiddappa","sequence":"first","affiliation":[]},{"given":"Amit Prakash","family":"Patil","sequence":"additional","affiliation":[]},{"given":"Geetha","family":"Kuntoji","sequence":"additional","affiliation":[]},{"given":"K. 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