Partitioning strategy for investigating the prediction capability of bed load transport under varied hydraulic conditions: Application of robust GWO-kernel-based ELM approach-Reference-Cited by-同舟云学术

Partitioning strategy for investigating the prediction capability of bed load transport under varied hydraulic conditions: Application of robust GWO-kernel-based ELM approach

Published:2022-04 Issue: Volume:84 Page:102136
ISSN:0955-5986
Container-title:Flow Measurement and Instrumentation
language:en
Short-container-title:Flow Measurement and Instrumentation

Author:

Roushangar Kiyoumars,Shahnazi Saman,Azamathulla Hazi Mohammad^ORCID

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Science Applications,Instrumentation,Modeling and Simulation

Reference45 articles.

1. An equation for bed-load transport capacities in gravel-bed rivers;Gao;J. Hydrol.,2011

2. The Effect of Riverbed Structure on Bed Load Transport in Mountain Streams;Zhang,2010

3. A general power equation for predicting bed load transport rates in gravel bed rivers, Water;Barry;Resour. Res.,2004

4. Effect of coarse surface layer on bed-load transport;Bathurst;J. Hydraul. Eng.,2007

5. Most influential parameters for the bed‐load sediment flux equations used in alluvial rivers;Khorram;J. Am. Water Resour. Assoc.,2010

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