Modeling the reduction percentage of the density current head flux using artificial intelligence

Density current is one of the most important factors in the sedimentation process of dams. Because this current is one of the important factors affecting the reduction of life efficiency of large dams, so understanding sedimentation patterns to manage the reservoir of dams is very effective. Accordingly, in this study, the reduction percentage of the density current head flux under the influence of trapezoidal permeable barriers (filled with sand grains with a diameter of 0.5 cm) is investigated also variable parameters effect such as discharge, slope, concentration and height of obstacles on density current control is examined experimentally, based on the results, the reduction percentage of the density current head flux was modeled using the artificial neural network feed-forward method and the classical multivariable regression method, and the performance of these two methods was compared. The results showed that the intelligent method of feed-forward artificial neural network has a significant advantage over the multivariable regression method in modeling the reduction percentage of the density current head flux.