Volume 22, Issue 66 (9-2022)                   jgs 2022, 22(66): 41-56 | Back to browse issues page


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Abolfathi D, Madadi A, Asghari S. Modeling of River Sediment Estimation Using Artificial Neural Network Method (Case Study: Vanai River). jgs 2022; 22 (66) :41-56
URL: http://jgs.khu.ac.ir/article-1-3169-en.html
1- university of Mohaghagh Ardebili
2- university of Mohaghagh Ardebili , aghil48madadi@yahoo.com
Abstract:   (5190 Views)

The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

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Type of Study: case report | Subject: Geomorphology

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