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pro vyhledávání: '"Bui, Van Hieu"'
Autor:
Dang, Thanh Quang, Tran, Ba Hoang, Le, Quyen Ngoc, Tanim, Ahad Hasan, Bui, Van Hieu, Mai, Son T., Thanh, Phong Nguyen, Anh, Duong Tran
Publikováno v:
In Environmental Modelling and Software January 2025 183
Autor:
Dang, Thanh Quang, Tran, Ba Hoang, Le, Quyen Ngoc, Dang, Thanh Duc, Tanim, Ahad Hasan, Pham, Quoc Bao, Bui, Van Hieu, Mai, Son T., Thanh, Phong Nguyen, Anh, Duong Tran
Publikováno v:
In Applied Soft Computing January 2024 150
Autor:
Le Xuan, Tu, Tran Ba, Hoang, Le Manh, Hung, Do Van, Duong, Minh Nguyen, Nguyet, Wright, David P., Bui, Van Hieu, Mai, Son T., Tran Anh, Duong
Publikováno v:
In Ocean Engineering 15 December 2020 218
Autor:
Bui, Van Hieu, Phan, Huyen Trang
Publikováno v:
Vietnam Journal of Computer Science (World Scientific); Nov2023, Vol. 10 Issue 4, p409-431, 23p
Akademický článek
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Publikováno v:
東海大学紀要. 海洋学部. 17:71-75
The Prediction of Fine Sediment Distribution in Gravel-Bed Rivers Using a Combination of DEM and FNN
Publikováno v:
Water, Vol 12, Iss 1515, p 1515 (2020)
Water
Volume 12
Issue 6
Water
Volume 12
Issue 6
Large amounts of fine sediment infiltration into void spaces of coarse bed material have the ability to alter the morphodynamics of rivers and their aquatic ecosystems. Modelling the mechanisms of fine sediment infiltration in gravel-bed is therefore
Autor:
Bui, Van Hieu
A framework, combined the Discrete Element Method (DEM) and Artificial Neural Network (ANN), was developed to predict the porosity and the fine sediment distribution. The porosity variation and fine sediment exchange were integrated to develop bed va
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______518::44eae9adb8134e2d52959e831cfab427
https://mediatum.ub.tum.de/doc/1521800/document.pdf
https://mediatum.ub.tum.de/doc/1521800/document.pdf
Publikováno v:
Water, Vol 11, Iss 7, p 1461 (2019)
Water
Volume 11
Issue 7
Water
Volume 11
Issue 7
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-