Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/44099
Title: Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization
Authors: Cheng, CT
Niu, WJ
Feng, ZK
Shen, JJ
Chau, KW 
Keywords: Artificial neural network
Daily runoff
Hybrid forecast
Quantum-behaved particle swarm optimization (QPSO)
Reservoir forecasting
Issue Date: 2015
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Water (Switzerland), 2015, v. 7, no. 8, p. 4232-4246 How to cite?
Journal: Water (Switzerland) 
Abstract: Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.
URI: http://hdl.handle.net/10397/44099
ISSN: 2073-4441
DOI: 10.3390/w7084232
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