The application of soft computing in the simulation of sediment flows

Document Type : Original Article

Authors

1 Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

2 Department of Water Sciences Engineering, College of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

3 Department of Water Sciences Engineering, College of Agriculture, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Abstract

Density flows are one of the most important factors in reducing the useful life of water structures, especially reservoir dams. Therefore, researchers have always been investigating solutions to eliminate these flows and increase the useful life of dams. One of the most practical methods known is to build an obstacle in the path of these currents. In this laboratory-numerical study, the effect of trapezoidal permeable obstacle (filled with 0.5 cm diameter grains) on the head of density flow is investigated and the effects of variables such as discharge, concentration, slope and height of the obstacle are studied. Based on the percentage reduction of the density flow head obtained from the experiments, the concentrated salt flow head is modeled using one of the soft computing methods known as the adaptive neural-fuzzy inference system (Anfis). Then, by comparing the results with the classical multivariable regression method, the performance of these two methods is compared. The error of training, validation and test data for the Anfis method are shown to be 0.07, 0.033 and 0.03, respectively, while for the multivariable regression method the mentioned errors are shown to be 0.12, 0.199 and 0.1084, respectively. Also, regression values for the training and test data for the Anfis method, are found to be 0.9954 and 0.9652 respectively whilst for the multivariable regression method, the mentioned parameters are shown to be 0.93108 and 0.90396 respectively which demonstrates the superiority and the efficiency of the adaptive neural-fuzzy inference system in modeling the head data.

Keywords

References

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History

  • Receive Date: 25 April 2021
  • Revise Date: 24 June 2021
  • Accept Date: 14 August 2021
  • Publish Date: 22 May 2021

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