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Short-term streamflow forecasting using the feature-enhanced regression modelBai, Yun, tehnolog ...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.Source: Water resources management. - ISSN 0920-4741 (Letn.33, št. nov., 2019, str. 4783-4797)Type of material - article, component part ; adult, seriousPublish date - 2019Language - englishCOBISS.SI-ID - 8969057
Author
Bai, Yun, tehnolog |
Bezak, Nejc, hidrologija |
Lebar, Klaudija |
Klun, Mateja, hidrotehnika, vodarstvo in okoljsko inženirstvo |
Zhang, Jin, 1983-
Topics
hidrotehnika |
dolgotrajni kratkoročni spomin |
avtoenkoder |
vhodna spremenljivka |
dnevni vtok v rezervoar |
napovedi |
long short-term memory |
stack autoencoder |
feature enhanced |
daily reservoir inflow |
forecast
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Database name | Field | Year |
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Links to authors' personal bibliographies | Links to information on researchers in the SICRIS system |
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Bai, Yun, tehnolog | |
Bezak, Nejc, hidrologija | 35409 |
Lebar, Klaudija | 38531 |
Klun, Mateja, hidrotehnika, vodarstvo in okoljsko inženirstvo | 38109 |
Zhang, Jin, 1983- |
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