Khabat KhosraviMir Jafar Sadegh SafariZohreh Sheikh KhozaniBrian Mark CrookstonAli GolkarianGolkarian, AliSheikh Khozani, ZohrehSafari, Mir Jafar SadeghKhozani, Zohreh SheikhCrookston, BrianKhosravi, Khabat2025-10-06202214327643, 143374791432-76431433-747910.1007/s00500-022-07073-02-s2.0-85128212793https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128212793&doi=10.1007%2Fs00500-022-07073-0&partnerID=40&md5=912206ad72bf2e5a27e79f9a05893812https://gcris.yasar.edu.tr/handle/123456789/8653https://doi.org/10.1007/s00500-022-07073-0Labyrinth 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 (C<inf>d</inf>) 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 C<inf>d</inf> 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 C<inf>d</inf> prediction respectively. According to NSE all developed algorithms provided accurate performances while ST-Kstar had the highest prediction power. © 2022 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessDischarge Coefficient, Hybridization, Labyrinth Weir, Machine Learning, Stacking Algorithm, Decision Tables, Decision Trees, Forecasting, Machine Learning, Optimization, Crest Length, Discharge Coefficients, Discharge Ratings, Geometric Complexity, Hybrid Algorithms, Hybridisation, Labyrinth Weirs, Machine-learning, Stacking Algorithms, Stackings, WeirsDecision tables, Decision trees, Forecasting, Machine learning, Optimization, Crest length, Discharge coefficients, Discharge ratings, Geometric complexity, Hybrid algorithms, Hybridisation, Labyrinth weirs, Machine-learning, Stacking algorithms, Stackings, WeirsStacking AlgorithmLabyrinth WeirHybridizationMachine LearningDischarge CoefficientArticle