Vaš brskalnik ne omogoča JavaScript!
JavaScript je nujen za pravilno delovanje teh spletnih strani. Omogočite JavaScript ali pa uporabite sodobnejši brskalnik.
Nacionalni portal odprte znanosti
Odprta znanost
DiKUL
slv
|
eng
Iskanje
Brskanje
Novo v RUL
Kaj je RUL
V številkah
Pomoč
Prijava
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
)
PDF - Predstavitvena datoteka,
prenos
(1,22 MB)
MD5: A7D13E36EDE6F1D9749C40844064313D
URL - Izvorni URL, za dostop obiščite
https://link.springer.com/content/pdf/10.1007%2Fs11269-019-02399-1.pdf
Galerija slik
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
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FGG - Fakulteta za gradbeništvo in geodezijo
Status publikacije:
V tisku
Različica publikacije:
Recenzirani rokopis
Založnik:
Springer Nature B. V.
Leto izida:
2019
Št. strani:
Str. 4783-4797
Številčenje:
Letn.33, št. nov.
PID:
20.500.12556/RUL-114378
UDK:
626/627
ISSN pri članku:
0920-4741
DOI:
10.1007/s11269-019-02399-1
COBISS.SI-ID:
8969057
Datum objave v RUL:
24.02.2020
Število ogledov:
1411
Število prenosov:
536
Metapodatki:
Citiraj gradivo
Navadno besedilo
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Kopiraj citat
Objavi na:
Gradivo je del revije
Naslov:
Water resources management
Skrajšan naslov:
Water resour. manag.
Založnik:
Reidel
ISSN:
0920-4741
COBISS.SI-ID:
512526105
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.
Podobna dela
Podobna dela v RUL:
Podobna dela v drugih slovenskih zbirkah:
Nazaj