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Title:

Correction: Combination of Discrete Element Method and Artificial Neural Network for Predicting Porosity of Gravel-Bed River. Water, Vol. 11, 2019, Article No. 1461].

Document type:
Zeitschriftenaufsatz
Author(s):
Bui, Van Hieu; Bui, Minh Duc; Rutschmann, Peter:
Abstract:
In gravel-bed rivers, monitoring porosity is vital for fluvial geomorphology assessment as well as in river ecosystem management. Conventional porosity prediction methods are restricting in terms of the number of considered factors and are also time-consuming. We present a framework, the combination of the Discrete Element Method (DEM) and Artificial Neural Network (ANN), to study the relationship between porosity and the grain size distribution. DEM was applied to simulate the 3D structure...     »
Keywords:
mathematical modelling; DEM; ANN; bed porosity; grain sorting; gravel-bed river
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Journal title:
Water
Year:
2020
Journal volume:
12
Journal issue:
2
Pages contribution:
Article No. 408, 1 page
Covered by:
Scopus; Web of Science
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.3390/w12020408
Publisher:
Molecular Diversity Preservation International (MDPI)
Publisher address:
Basel, Switzerland
E-ISSN:
2073-4441
Impact Factor:
2.544 (2019)
Copyright statement:
Open Access. Copyright: © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Format:
Text
CC license:
by, http://creativecommons.org/licenses/by/4.0
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