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Titel:

Long Short-Term Memory for Predicting Daily Suspended Sediment Concentration.

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Kaveh, Keivan; Kaveh, Hamid; Bui, Minh Duc; Rutschmann, Peter:
Abstract:
Frequent and accurate estimation of suspended sediment concentration (SSC) in surface waters and hydraulic schemes is of prime importance for proper design, operation and management of many hydraulic projects. in the present study, a long short-term memory (LSTM) was considered for predicting daily suspended sediment concentration in a river. The LSTM extends recurrent neural network with memory cells, instead of recurrent units, to store and output information, easing the learning of temporal r...     »
Stichworte:
Long short-term memory; Fuzzy inference system; Schuylkill river; Suspended sediments
Dewey Dezimalklassifikation:
620 Ingenieurwissenschaften
Zeitschriftentitel:
Engineering with Computers
Jahr:
2021
Band / Volume:
37
Seitenangaben Beitrag:
2013-2027
Nachgewiesen in:
Scopus; Web of Science
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1007/s00366-019-00921-y
Verlag / Institution:
Springer
Verlagsort:
London, UK
Print-ISSN:
0177-0667
E-ISSN:
1435-5663
Impact Factor:
7.963 (2020)
Copyright Informationen:
Copyright: © Springer-Verlag London Ltd., part of Springer Nature 2020. All rights reserved.
Format:
Text
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