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River suspended sediment load prediction based on river discharge information: application of newly developed data mining models

Salih, Sinan Q., Sharafati, Ahmad, Khosravi, Khabat, Faris, Hossam, Kisi, Ozgur, Tao, Hai, Ali, Mumtaz and Yaseen, Zaher Mundher 2020, River suspended sediment load prediction based on river discharge information: application of newly developed data mining models, Hydrological sciences journal, vol. 65, no. 4, pp. 624-637, doi: 10.1080/02626667.2019.1703186.

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Title River suspended sediment load prediction based on river discharge information: application of newly developed data mining models
Author(s) Salih, Sinan Q.
Sharafati, Ahmad
Khosravi, Khabat
Faris, Hossam
Kisi, Ozgur
Tao, Hai
Ali, MumtazORCID iD for Ali, Mumtaz orcid.org/0000-0002-6975-5159
Yaseen, Zaher Mundher
Journal name Hydrological sciences journal
Volume number 65
Issue number 4
Start page 624
End page 637
Total pages 14
Publisher Taylor & Francis
Place of publication Abingdon, Eng.
Publication date 2020
ISSN 0262-6667
2150-3435
Keyword(s) Science & Technology
Physical Sciences
Water Resources
data mining models
suspended sediment load
river hydrology
stochasticity
watershed management
SUPPORT VECTOR MACHINE
FUZZY INFERENCE SYSTEM
COMPRESSIVE STRENGTH
DIFFUSION-MODEL
NEURAL-NETWORKS
TREE
TRANSPORT
ALGORITHM
FLOW
PERFORMANCE
Language eng
DOI 10.1080/02626667.2019.1703186
Indigenous content off
Field of Research 0406 Physical Geography and Environmental Geoscience
0905 Civil Engineering
0907 Environmental Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30133735

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Created: Tue, 21 Jan 2020, 12:03:07 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.