Modeling the Behavior of Density Current with Machine Learning Algorithms

Subject Areas : Subjects

Mehdi Derakhshannia ¹ (Ph.D. Candidate, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Mehdi Ghomeshi ² (Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran)
Seyed Saeid Eslamian ³ (Professor, Department of Civil Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. and Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran)
Seyed Mahmood Kashefipour ⁴ (Professor, Department of Water Structures, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran)

Received: 2021-01-19 Accepted : 2021-09-01 Published : 2022-10-23

Keywords: density current, sedimentation, Head reduction percentage, Adaptive Neural-Fuzzy Inference System, feed-forward artificial neural network,

Abstract :

Abstract
Introduction: Density current is one of the factors influencing the transfer of sediments to reservoirs of dams. One of the practical methods to control sediments is to build an obstacle in the path of these currents.
Methods: In this laboratory research, the behavior of the Density current under the effect of cylindrical obstacles made of wood with a diameter of 1.5 cm and a height of 30 cm (more than the height of the body of the Density current) was evaluated. Therefore, by considering variables such as floor slope, concentration and discharge, the values of the density current head were determined. Machine learning algorithms such as adaptive neural fuzzy inference system and artificial neural network were used to model the results.
Findings: Based on the results, the density salt flow head was modeled using machine learning algorithms such as adaptive fuzzy neural inference system and artificial neural network and the performance of these two methods were compared. The results showed that machine learning algorithms are useful in modeling the density salt flow head. And the regression of the adaptive neural fuzzy inference system for the training and test data was 0.99 and the regression of the artificial neural network was 0.94 and 0.91, respectively.
Conclusion: By comparing the two methods, it was found that the adaptive neural-fuzzy inference system is more effective in modeling the percent reduction of the head of Density current than the feed-forward artificial neural network method.

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