izpis_h1_title_alt

Short-term streamflow forecasting using the feature-enhanced regression model
ID Bai, Yun (Avtor), ID Bezak, Nejc (Avtor), ID Lebar, Klaudija (Avtor), ID Klun, Mateja (Avtor), ID Zhang, Jin (Avtor)

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Izvleček
Reservoir inflow forecasting is extremely important for the management of a reservoir. In practice, accurate forecasting depends on the feature learning performance. To better address this issue, this paper proposed a feature-enhanced regression model (FER), which combined stack autoencoder (SAE) with long short-term memory (LSTM). This model had two constituents: (1) The SAE was constructed to learn a representation as close as possible to the original inputs. Through deep learning, the enhanced feature could be captured sufficiently. (2) The LSTM was established to simulate the mapping between the enhanced features and the outputs. Under recursive modeling, the patterns of correlation in the short term and dependence in the long term were considered comprehensively. To estimate the performance of the FER model, two historical daily discharge series were investigated, i.e., the Yangtze River in China and the Sava Dolinka River in Slovenia. The proposed model was compared with other machine-learning methods (i.e., the LSTM, SAE-based neural network, and traditional neural network). The results demonstrated that the proposed FER model yields the best forecasting performance in terms of six evaluation criteria. The proposed model integrates the deep learning and recursive modeling, and thus being beneficial to exploring complex features in the reservoir inflow forecasting. Moreover, for smaller catchments with significant torrential characteristics, more data are needed (e.g., at least 20 years) to effectively train the model and to obtain accurate flood-forecasting results.

Jezik:Angleški jezik
Ključne besede:long short-term memory, stack autoencoder, feature enhanced, daily reservoir inflow, forecast
Vrsta gradiva:Znanstveno delo (r2)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FGG - Fakulteta za gradbeništvo in geodezijo
Leto izida:2019
Status objave pri reviji:V tisku
Verzija članka:Postprint, avtorjeva končna recenzirana različica članka, sprejeta v objavo
Založnik:Springer Nature B. V.
Št. strani:Str. 4783-4797
Številčenje:Letn.33, št. nov.
PID:20.500.12556/RUL-114378 Povezava se odpre v novem oknu
UDK:626/627
ISSN pri članku:0920-4741
DOI:10.1007/s11269-019-02399-1 Povezava se odpre v novem oknu
COBISS.SI-ID:8969057 Povezava se odpre v novem oknu
Datum objave v RUL:24.02.2020
Število ogledov:774
Število prenosov:365
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
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Gradivo je del revije

Naslov:Water resources management
Skrajšan naslov:Water resour. manag.
Založnik:Reidel
ISSN:0920-4741
COBISS.SI-ID:512526105 Povezava se odpre v novem oknu

Licence

Licenca:CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
Opis:Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.
Začetek licenciranja:24.02.2020

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:hidrotehnika, dolgotrajni kratkoročni spomin, avtoenkoder, vhodna spremenljivka, dnevni vtok v rezervoar, napovedi

Projekti

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:P2-0180
Naslov:Vodarstvo in geotehnika: orodja in metode za analize in simulacije procesov ter razvoj tehnologij.

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