Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

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 OPEN ACCESSWater 2015, 7 4233 accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.

Comparative Studies of Different Imputation Methods for Recovering Streamflow Observation

“…Therefore, more complex nonlinear models such as artificial neural networks (ANNs) have been applied for better estimation in recovering streamflow [14][15][16]. Previous studies [9,17,18] have reported as well that the self-organizing map (SOM), an unsupervised ANN, showed satisfactory imputation results.…”

Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff

“…First, to improve the model performance, the MLMs have been combined with statistical methods, including phase-space reconstruction [22,23], principal component analysis [24,25], fuzzy c-means clustering [7,22], k-means clustering [26,27], self-organizing map (SOM) [28,29] and bootstrap [30]. Second, the MLMs have been coupled with evolutionary optimization algorithms, including genetic algorithm (GA) [31,32], particle swarm optimization (PSO) [11,33], artificial bee colony [34], bat algorithm [35], and firefly algorithm [36]. The addressed algorithms were very helpful for efficient model learning and optimal parameter searching.…”

Health Condition Evaluation for a Shearer through the Integration of a Fuzzy Neural Network and Improved Particle Swarm Optimization Algorithm

“…In order to further prove the optimization performance of the proposed IPSO in training the FNN model, the basic PSO [26], quantum-behaved PSO (QPSO) [27], opinion leader-based QPSO (OLB-QPSO) [28], and Laplace PSO (LPSO) [29] were employed to optimize the parameters in the FNN model. The parameters were set according to the relevant literatures.…”