Modeling of River Sediment Estimation Using Artificial Neural Network Method (Case Study: Vanai River)
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 MLPchr('39')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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Flood Potential Assessment in the Kuzet-Topraqi Watershed Sub-basins Using the Extended Flash Flood Model
*, Amirhesam Pasban, Behrouz Nezafat Taklhe
Journal of Sustainable Urban and Regional Development Studies, Autumn 2025 -
Comparative comparison of the performance of CODAS, MABAC algorithms in zoning the risk of land subsidence using environmental indicators (case study: the boundaries of the cities of Alborz province)
Sayyad Asghari *, Fariba Esfandiary Darabad, Mehdi Faal Naziri, Batool Zeinali
quantitative geomorphological researches, Spring 2025 -
Investigating the effects of land use changes on soil erosion in Meshkinshahr County
Elnaz Piroozi, *, Batool Zeinali
Environmental Erosion Researches, Spring 2025 -
Landslide risk zoning in Sain Pass (Ardabil-Sarab communication axis)
*, Davar Taghizadeh
Journal of Geography and Human Relations,