Intelligent Modeling of Salty Density Current in the Presence of Permeable Obstacles

Density current is one of the most important factors in the sedimentation process of dams. Increased sediment will reduce dam storage capacity and makes significant challenges for relevant engineers. Therefore, understanding the dynamics of density fluids and related sediment patterns is very efficient for dam reservoir management.

The purpose of this study was to create an intelligent model with appropriate adaptation to laboratory data so that, it can be used in future designs with different variables. Therefore, in this study, the percentage of reduction of density salt current head under the influence of trapezoidal permeable obstacles (aggregates with a diameter of 1 cm), taking into account variables such as discharge, slope, concentration and height of obstacles in laboratory.

Based on the results, the density salt current head was modeled using the artificial neural network feed-forward method and the classical multivariate regression method, and the performance of these two methods was compared. The results showed that the intelligent feed neural network intelligent method in modeling the percentage reduction of density salt current head is significantly superior to the multivariate regression method so that the training, calibration and test regression values ​​ are 0.99, 0.98 and 0.98 were obtained for neural network and 0.92, 0.91 and 0.91 for multivariate regression, respectively.

The performance of the artificial neural network is much better than the multivariate regression method.