Using soft computing and machine learning algorithms to predict the discharge coefficient of curved labyrinth overflows

Kwok Wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)


This research aims to estimate the overflow capacity of a curved labyrinth using different intelligent prediction models, namely the adaptive neural-fuzzy inference system, the support vector machine, the M5 model tree, the least-squares support vector machine and the least-squares support vector machine–bat algorithm (LSSVM-BA). A total of 355 empirical data for 6 different congressional overflow models were extracted from the results of a laboratory study on labyrinth overflow models. The parameters of the upstream water head to overflow ratio, the lateral wall angle and the curvature angle were used to estimate the discharge coefficient of curved labyrinth overflows. Based on various statistical evaluation indicators, the results show that those input parameters can be relied upon to predict the discharge coefficient. Specifically, the LSSVM-BA model showed the best prediction accuracy during the training and test phases. Such a low-cost prediction model may have a remarkable practical implication as it could be an economic alternative to the expensive laboratory solution, which is costly and time-consuming.
Original languageEnglish
Pages (from-to)1002-1015
Number of pages14
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 2021


  • Discharge coefficient
  • artificial intelligence
  • labyrinth overflow
  • machine learning
  • support vector machine (SVM)

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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