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

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Abstract
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.

Language:English
Keywords:long short-term memory, stack autoencoder, feature enhanced, daily reservoir inflow, forecast
Work type:Scientific work (r2)
Tipology:1.01 - Original Scientific Article
Organization:FGG - Faculty of Civil and Geodetic Engineering
Year:2019
Publisher:Springer Nature B. V.
Number of pages:Str. 4783-4797
Numbering:Letn.33, št. nov.
UDC:626/627
ISSN on article:0920-4741
DOI:10.1007/s11269-019-02399-1 This link opens in a new window
COBISS.SI-ID:8969057 This link opens in a new window
Views:445
Downloads:223
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Record is a part of a journal

Title:Water resources management
Shortened title:Water resour. manag.
Publisher:Reidel
ISSN:0920-4741
COBISS.SI-ID:512526105 This link opens in a new window

Document is financed by a project

Funder:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije (ARRS)
Project no.:P2-0180
Name:Vodarstvo in geotehnika: orodja in metode za analize in simulacije procesov ter razvoj tehnologij.

Licences

License:CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:24.02.2020

Secondary language

Language:Slovenian
Keywords:hidrotehnika, dolgotrajni kratkoročni spomin, avtoenkoder, vhodna spremenljivka, dnevni vtok v rezervoar, napovedi

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