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.

According to the traffic conditions in the typical freeway tunnel group in China, an artificial neural network model is constructed for the purpose of predicting the operating speed in freeway tunnel group in this paper. In this model, some input variables are selected from four aspects, including time factors, traffic dynamic factors, road conditions and tunnel environment, and the output variable is the operating speed. Then the sensitivity analysis method is selected to study the effects of input variables on output variable. The results show that this algorithm can avoid the difficulty of constructing traffic flow model comparing to the traditional algorithm, and it is suitable to realize online modeling for speed limit of freeway tunnel group. Results of this research are practical and effective, and it may provide a theoretical foundation for speed limit of freeway tunnel group.