Estimation of bar parameters occurred by cross-shore sediment transport using artificial intelligence methods

Sediment transport perpendicular to the shore is a crucial factor influencing coastal morphology. Berm and bar formation in both summer and winter profiles significantly impacts coastal structure. Considering such factors in the design of groynes, breakwaters, and similar structures has become essential both for the structures themselves and to prevent potential negative consequences on the shore. In this study, using data obtained from experimental studies, the distances between the starting and ending points of sandbars formed in winter profiles and the shoreline were estimated using fuzzy logic, an adaptive network-based fuzzy inference system, support vector machines, and multiple linear regression models. In the analysis studies, the ANFIS model demonstrated the best performance for both the distance output between the bar's starting point and the shoreline and the distance output between the bar's ending point and the shoreline. Models based on the distance output between the bar's ending point and the shoreline showed better results than models based on the distance output between the bar's starting point and the shoreline.