Machine Learning Approach to Predict Discharge Magnification of Triangular Labyrinth Weir

Abstract

Triangular labyrinth weirs are widely used in hydraulic structures because they can pass higher flow rates under limited water head compared to conventional weirs. However, predicting how much additional discharge they can convey relative to a standard configuration is challenging due to complex flow behaviour and geometric effects. Existing empirical equations are often limited in their applicability to specific conditions. In this study, four data-driven modeling approaches-Gaussian Process Regression (GPR), Support Vector Machine (SVM), Function Fitting Network (FITNET), and Multi-Layer Perceptron (MLP)-were developed and compared to estimate the relative increase in discharge for triangular labyrinth weirs. The analysis is based on 186 experimental observations covering a range of sidewall angles (15°, 25°, 30°, and 40°) and different numbers of cycles (3 to 5). The models use key geometric and hydraulic characteristics, expressed in normalized form, to capture the influence of flow depth and weir geometry. Model performance was evaluated using standard statistical indicators of prediction accuracy. Among the tested approaches, GPR provided the most accurate predictions, significantly reducing error compared to the other models. The results demonstrate that data-driven techniques can effectively represent the complex relationship between flow conditions and discharge performance. The proposed approach offers a reliable tool for the design and optimization of triangular labyrinth weirs, with potential applications in flood control and hydraulic structure safety.