Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43766
Title: A novel hybrid neural network based on continuity equation and fuzzy pattern-recognition for downstream daily river discharge forecasting
Authors: Chen, XY
Chau, KW 
Wang, WC
Keywords: Artificial neural network
Continuity equation
Daily river discharge forecasting
Fuzzy pattern-recognition
Particle swarm optimization
Storage reservoir
Issue Date: 2015
Publisher: International Water Association Publishing
Source: Journal of hydroinformatics, 2015, v. 17, no. 5, p. 733-744 How to cite?
Journal: Journal of hydroinformatics 
Abstract: Forecasting of river discharge is crucial in hydrology and hydraulic engineering owing to its use in the design and management of water resource projects. The problem is customarily settled with data-driven models. In this research, a novel hybrid model which combines continuity equation and fuzzy pattern-recognition concept with artificial neural network (ANN), is presented for downstream river discharge forecasting in a river network. Time-varying water storage in a river station and fuzzy feature of river flow are considered accordingly. To verify the proposed model, traditional ANN model, fuzzy pattern-recognition neural network model, and hydrological modeling network model have been employed as the benchmark models. The root mean squared error, Nash-Sutcliffe efficiency coefficient and accuracy are adopted as evaluation criteria. The proposed hybrid model is applied to compute downstream river discharge in the Yellow River, Georgia, USA. Results indicate that the proposed hybrid model delivers better performance, which can effectively improve forecasting capability at the studied station. It is, therefore, proposed as a novel model for downstream river discharge forecasting because of its highly nonlinear, fuzzy and non-stationary properties.
URI: http://hdl.handle.net/10397/43766
ISSN: 1464-7141
DOI: 10.2166/hydro.2015.095
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

6
Last Week
0
Last month
Citations as of Sep 8, 2017

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
Citations as of Sep 15, 2017

Page view(s)

32
Last Week
0
Last month
Checked on Sep 17, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.