Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model-Reference-Cited by-同舟云学术

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Short-Term Streamflow Forecasting Using the Feature-Enhanced Regression Model

Published:2019-11 Issue:14 Volume:33 Page:4783-4797
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Bai Yun,Bezak Nejc,Sapač Klaudija,Klun Mateja,Zhang Jin

Funder

National Natural Science Foundation of China

Ministry of Science and Technology of the People's Republic of China

Javna Agencija za Raziskovalno Dejavnost RS

Publisher

Springer Science and Business Media LLC

Subject

Water Science and Technology,Civil and Structural Engineering

Link

http://link.springer.com/content/pdf/10.1007/s11269-019-02399-1.pdf

Reference47 articles.

1. Allawi MF, Jaafar O, Hamzah FM et al (2018) Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system : a case study for a semi-arid region. Theor Appl Climatol 134:545–563

2. ARSO (2019a) Klimatološka povprečja 1981–2010. http://www.meteo.si/met/sl/climate/tables/n-ormals_81_10/. Accessed 24 May 2019

3. ARSO (2019b) Arhiv hidroloških podatkov. Arhiv površinskih voda. http://vode.arso.gov.si/hi-darhiv/pov_arhiv_tab.php. Accessed 24 May 2019

4. Badrzadeh H, Sarukkalige R, Jayawardena AW (2016) Improving Ann-based short-term and long-term seasonal river flow forecasting with signal processing techniques. River Res Appl 32:245–256

5. Bai Y, Chen Z, Xie J et al (2016a) Daily reservoir inflow forecasting using multiscale deep feature learning with hybrid models. J Hydrol 532:193–206

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