Stacking ensemble-based hybrid algorithms for discharge computation in sharp-crested labyrinth weirs

Abstract

Labyrinth weirs are utilized to transport a greater discharge during floods in contrast to conventional weirs due to their increased weir crest length. Nevertheless, due to the increased geometric complexity of labyrinth weirs, determination of accurate discharge coefficients and accordingly, head-discharge ratings are quite essential issues in practical application. Hence, as a first step the present study proposes the following eight standalone algorithms: decision table (DT), Kstar, least median square (LMS), M5 prime (M5P), M5 rule (M5R), pace regression (PR), random forest (RF) and sequential minimal optimization (SMO). Then, applying the stacking (ST) algorithm, these standalone models were hybridized to predict the discharge coefficient (Cd) for sharp-crested labyrinth weirs. Potential/effective variables were constructed in the form of several independent dimensionless parameters (i.e., θ, h/W, L/B, L/h, Froude number (Fr), B/W and L/W) to predict Cd as an output. The accuracy of the developed models was examined in terms of different statistical visually based and quantitative-based error measurement criteria. The results illustrate that h/W and B/W parameters have the highest and lowest effect on the Cd prediction, respectively. According to NSE, all developed algorithms provided accurate performances, while ST-Kstar had the highest prediction power.