Comparative analysis of soft computing techniques RBF, MLP, and ANFIS with MLR and MNLR for predicting grade-control scour hole geometry

Hossien Riahi-Madvar, Majid Dehghani, Akram Seifi, Ely Salwana, Shahaboddin Shamshirband, Amir Mosavi, Kwok wing Chau

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


The main aims and contributions of the present paper are to use new soft computing methods for the simulation of scour geometry (depth/height and locations) in a comparative framework. Five models were used for the prediction of the dimension and location of the scour pit. The five developed models in this study are multilayer perceptron (MLP) neural network, radial basis functions (RBF) neural network, adaptive neuro fuzzy inference systems (ANFIS), multiple linear regression (MLR), and multiple non-linear regression (MNLR) in comparison with empirical equations. Four non-dimensional geometry parameters of scour hole shape are predicted by these models including the maximum scour depth (S), the distance of S from the weir (XS), the maximum height of downstream deposited sediments (hd), and distance of hd from the weir (XD). The best results over train data derived for XS/Z and hd/Z by the MLP model with R2 are 0.95 and 0.96 respectively; the best predictions for S/Z and XD/Z are from the ANFIS model with R2 0.91 and 0.96 respectively. The results indicate that the application of MLP and ANFIS results in the accurate prediction of scour geometry for the designing of stable grade control structures in alluvial irrigation channels.

Original languageEnglish
Pages (from-to)529-550
Number of pages22
JournalEngineering Applications of Computational Fluid Mechanics
Issue number1
Publication statusPublished - 1 Jan 2019


  • alluvial channels
  • artificial intelligence
  • big data
  • grade control structure
  • radial basis functions
  • scour geometry

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation

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