Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods-Reference-Cited by-同舟云学术

Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods

Published:2018-11-27 Issue:3 Volume:17 Page:333-352
ISSN:1571-5124
Container-title:International Journal of River Basin Management
language:en
Short-container-title:International Journal of River Basin Management

Author:

Shaghaghi Saba¹,Bonakdari Hossein¹^ORCID,Gholami Azadeh¹,Kisi Ozgur²,Binns Andrew³,Gharabaghi Bahram³^ORCID

Affiliation:

1. Department of Civil Engineering, Razi University, Kermanshah, Iran

2. School of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia

3. School of Engineering, University of Guelph, Guelph, Canada

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15715124.2018.1546731

Reference68 articles.

1. Updated regime equations for alluvial Egyptian canals

2. Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan

3. Effect of Nonuniformity of Flow on Hydraulic Geometry Relations

4. Hydraulic Geometry Relations for Stable Channel Design

5. Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland

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