Investigation of the accuracy of multilayer perceptron network and radial base function in estimating river sediment(Case study: Zayandehrud)

Estimating the amount of sediment by the river is one of the topics that has been considered by many researchers since the past. Reduction of the dam reservoir capacity because of sediments has different effects on different sections and causes adverse effects on the water rights that were initially agreed upon, which will impose several economic and specific consequences. This study aims to model and estimate the amount of suspended sediment using existing experimental equations and new methods called black box.

The discharge (volumetric flow rate) related to Zayandehrud River in Eskandari station, one of the hydrological measuring stations, has been used to estimate the amount of sediment. For this purpose, water discharge and sediment rate are used as input and output, respectively.

According to the obtained results, it is concluded that the RBF network has better performance due to less error in the test stage, but the MLP network seems to have a better performance considering other parameters and the error in the TRAIN stage.

Finally, after modeling by using neural networks, the Einstein relationship, and the sediment measurement curve, it is inferred that neural networks are more accurate to estimate the amount of sediment.