CIVILICA We Respect the Science
Publisher of Iranian Journals and Conference Proceedings
Paper
title

An optimal Adaptive Neural Fuzzy Inference System (ANFIS) model and regression relations to predict stable channel geometry in rivers gravel bed

Credit to Download: 1 | Page Numbers 10 | Abstract Views: 174
Year: 2015
COI code: IREC10_033
Paper Language: English

How to Download This Paper

For Downloading the Fulltext of CIVILICA papers please visit the orginal Persian Section of website.

Authors An optimal Adaptive Neural Fuzzy Inference System (ANFIS) model and regression relations to predict stable channel geometry in rivers gravel bed

  Azadeh Gholami - Ph.D. Candidate, Department of Civil Engineering, Razi University, Kermanshah, Iran
  Hossein Bonakdari - Associate. Prof, Department of Civil Engineering, Razi University, Kermanshah, Iran.
  Saba Shaghaghi - M.Sc. Student, Department of Civil Engineering, Razi University, Kermanshah, Iran,
  Isa Ebtehaj - Ph.D. Candidate, Department of Civil Engineering, Razi University, Kermanshah, Iran

Abstract:

Hydraulic geometry of a river has primary importance in the design, planning, management and river training in river engineering science. In investigation of stable channels dimensions, the most presented relations are based on statistical and theoretical methods that don’t have more accuracy. In last decades, using soft computing methods or artificial neural methods because of high accuracy and fewer time and cost are interested by different science researches. In the present paper, using Adaptive Neural Fuzzy Inference System (ANFIS) model, the accuracy of regression relations to predict width, depth and slope of stable channels are improved. A set of observed data (including 85 cross section data) are used to train and test ANFIS models and also to fit regression relations. The two models efficiency are evaluated and compared with observed data. Results show that ANFIS models with R2 values of 0.9224, 0.7464 and 0.9264 show a high accuracy to predict width, depth and slope of stable channels, respectively. Also, the mean absolute relative error (MARE) values in regression relation are 73, 57 and 50 times higher than ANFIS models in predicting width, depth and slope, respectively. Therefore, using ANFIS model causes to improve regression equations performance and its results can be used in the design of Executive channels.

Keywords:

ANFIS model, regression relations, models efficiency, observed data, stable channel geometry

Perma Link

https://www.civilica.com/Paper-IREC10-IREC10_033.html
COI code: IREC10_033

how to cite to this paper:

If you want to refer to this article in your research, you can easily use the following in the resources and references section:
Gholami, Azadeh; Hossein Bonakdari; Saba Shaghaghi & Isa Ebtehaj, 2015, An optimal Adaptive Neural Fuzzy Inference System (ANFIS) model and regression relations to predict stable channel geometry in rivers gravel bed, 10th International River Engineering Conference, اهواز, دانشگاه شهيد چمران اهواز, https://www.civilica.com/Paper-IREC10-IREC10_033.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Gholami, Azadeh; Hossein Bonakdari; Saba Shaghaghi & Isa Ebtehaj, 2015)
Second and more: (Gholami; Bonakdari; Shaghaghi & Ebtehaj, 2015)
For a complete overview of how to citation please review the following CIVILICA Guide (Citation)

Scientometrics

The University/Research Center Information:
Type: state university
Paper No.: 7870
in University Ranking and Scientometrics the Iranian universities and research centers are evaluated based on scientific papers.

Research Info Management

Export Citation info of this paper to research management softwares

New Related Papers

Iran Scientific Advertisment Netword

Share this paper

WHAT IS COI?

COI is a national code dedicated to all Iranian Conference and Journal Papers. the COI of each paper can be verified online.