Estimating width of the stable channels using multivariable mathematical models

Neda Yousefi, Saeed Reza Khodashenas*, Saeid Eslamian, Zahra Askari

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Predicting behavior and the geometry of the channels and alluvial rivers in which the erosion and sediment transport are in equilibrium is one of the most important topics in river engineering. Various researchers have proposed empirical equations to estimate stable river width (W). In this research, empirical equations were examined and tested with a comprehensive available data set consisting of 1644 points collected from 29 stable rivers in various parts of the world. The data set covers a wide range of flow conditions, river geometry, and bed sediments. This data set is classified in two groups (W < 600 m and W ≥ 600 m) for presenting the new models. The new linear and nonlinear multivariable equations were fitted to these two groups, and the best models were selected by preliminary tests and diagnostic determined for each group. The determination coefficient of these models ranged from 0.87 to 0.96. The results show that the models presented in this paper are more accurate with respect to the previously presented models. In the second part, “Artificial neural networks,” perceptron was used and a new methodology for estimating stable channel width was developed. Comparison of the statistical methods presented in this paper and the results of perceptron neural network revealed preferential recent method.

Original languageEnglish
Article number321
Pages (from-to)1-11
Number of pages11
JournalArabian Journal of Geosciences
Volume9
Issue number4
DOIs
Publication statusPublished - Apr 2016
Externally publishedYes

Keywords

  • alluvial rivers
  • artificial neural networks
  • multivariate regression
  • perceptron
  • stable width

Fingerprint

Dive into the research topics of 'Estimating width of the stable channels using multivariable mathematical models'. Together they form a unique fingerprint.

Cite this