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Daily runoff forecasting using a cascade long short-term memory model that considers different variables
ID Bai, Yun (Avtor), ID Bezak, Nejc (Avtor), ID Zheng, Chuan-Bo (Avtor), ID Li, Chuan (Avtor), ID Lebar, Klaudija (Avtor), ID Zhang, Jin (Avtor)

URLURL - Izvorni URL, za dostop obiščite https://doi.org/10.1007/s11269-020-02759-2 Povezava se odpre v novem oknu

Izvleček
Accurate forecasts of daily runoff are essential for facilitating efficient resource planning and management of a hydrological system. In practice, daily runoff is needed for various practical applications and can be predicted using precipitation and evapotranspiration data. To this end, a long short-term memory (LSTM) under a cascade framework (C-LSTM) approach is proposed for forecasting daily runoff. This C-LSTM model is composed of a 2-level forecasting process. (1) In the first level, an LSTM is established to learn the relationship between the precipitation and evapotranspiration at present and to learn several meteorological variables one day in advance. (2) In the second level, an LSTM is constructed to forecast the daily runoff using the historical and simulated precipitation and evapotranspiration data produced by the first LSTM. Through cascade modeling, the complex features of the numerous targets in the different stages can be sufficiently extracted and learned by multiple models in a single framework. In order to evaluate the performance of the C-LSTM approach, four mesoscale sub-catchments of the Ljubljanica River in Slovenia were investigated. The results indicate that based on the root-mean-square error, the Pearson correlation coefficient, and the NashSutcliffe model efficiency coefficient, the proposed model yields better results than two other tested models, including the normal LSTM and other neural network approaches. Based on the results of this study, we conclude that the LSTM under the cascade architecture is a valuable approach and can be regarded as a promising model for forecasting daily runoff.

Jezik:Angleški jezik
Ključne besede:long short-term memory, cascade framework, meteorological conditions, precipitation-evapotranspiration pattern, daily runoff forecast
Vrsta gradiva:Znanstveno delo (r2)
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FGG - Fakulteta za gradbeništvo in geodezijo
Leto izida:2021
Status objave pri reviji:Objavljeno
Verzija članka:Založnikova različica članka
Poslano v recenzijo:08.07.2020
Datum sprejetja članka:29.12.2020
Datum objave:19.02.2021
Št. strani:Str. 1167-1181
Številčenje:ǂLetn.ǂ35, št. 293
PID:20.500.12556/RUL-127410 Povezava se odpre v novem oknu
UDK:551.5:556
ISSN pri članku:0920-4741
DOI:10.1007/s11269-020-02759-2 Povezava se odpre v novem oknu
COBISS.SI-ID:54074115 Povezava se odpre v novem oknu
Datum objave v RUL:04.06.2021
Število ogledov:447
Število prenosov:162
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

Sekundarni jezik

Jezik:Slovenski jezik
Ključne besede:data mining, kaskadni sistem, meteorološki pogoji, padavine-evapotranspiracija, pretok

Projekti

Financer:ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Naslov:Bilateralni projekt Slovenija-Kitajska, J2-7322, P2-0180

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