Prediction of inlet-to-outlet width ratio of type-A piano key weir using fuzzy neural network (FNN)

Abstract

A piano key weir (PKW) is a nonlinear (labyrinth-type) weir with a small spillway footprint and a large discharge carrying capacity. It enables water bodies to continue functioning at elevated supply levels while causing no damage to dam structures, resulting in increased storage. PKW's geometrical structure is extremely complex, and geometrical aspects have a significant impact on its efficiency as well as on energy dissipation. Among them, relative width ratio (Wi/Wo) (i.e. inlet-to-outlet key width ratio) is a critical parameter that affects the PKW's discharge efficiency and energy dissipation across the weir significantly. In this article, a fuzzy neural network (FNN) is used to predict the width of the PKW's inlet-to-outlet key and understand the resulting hydraulic behaviours. The dataset used in this study was collected experimentally, which adds to the study's authenticity because it is not a conventional dataset. The model's performance is evaluated by the root mean square error (RMSE) and mean absolute error (MAE); both values are 0.0305 and 0.0222, respectively. According to the dataset, these scores tell the model's reliability as it is in the ideal range.