Predictive model-based for the critical submergence of horizontal intakes in open channel flows with different clearance bottoms using CART, ANN and linear regression approaches

  • Authors:
  • Mohammad Karim Ayoubloo;H. Md. Azamathulla;Ebrahim Jabbari;Morteza Zanganeh

  • Affiliations:
  • Tehran Firefighting and Safety Services Organization, Tehran, Iran;River Engineering and Urban Drainage Research Centre (REDAC), Universiti Sains Malaysia, Engineering Campus, 14300 Nibong Tebal, Penang, Malaysia;College of Civil Engineering, Iran University of Science and Technology, Narmak, P.O. Box 16765-163, Tehran, Iran;College of Civil Engineering, Iran University of Science and Technology, Narmak, P.O. Box 16765-163, Tehran, Iran

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

Quantified Score

Hi-index 12.05

Visualization

Abstract

This study presents the development of classification and regression tree (CART), artificial neural network (ANN) and linear regression approaches to predict the critical submergence in an open channel flow for different clearance bottoms. To use the models for application purposes and cover the wide range of inputs, the nondimensional parameters are employed to train and test. The testing results show that all three approaches satisfactorily estimate the critical submergence with margin differences. Also, committee models arithmetic mean-based for the testing results of the tree mentioned approaches are presented as the best models. A comparison between the present study and empirical approaches is carried out which indicates the proposed approaches outperform the empirical formulas expressed in the literature. In addition, committee models are presented as the more generalized approaches by AIC criterion. The results also indicate that the variations of the best approach (committee)-predicted and observed the normalized critical submergence with the intake pipe diameter versus the number of the testing data follow favorably a similar trend. Finally, a sensitivity analysis shows that the ratio of the velocity in an intake pipe to the velocity in a channel is the significant parameter in the estimation of the critical submergence.