摘要
Abstract
In light of the problems that the prediction accuracy of a linear combination model is not high, the weight of a single prediction model is hard to determine, and the function of a nonlinear combined prediction model is hard to construct, a multiple combined annual runoff prediction model was put forward based on the principle of neural networks such as BP, Elman, RBF, GRNN, so as to maximize the useful information between the input vectors, and give full play to the characteristics of neural network models, such as their nonlinear mapping ability. Using the results of four single prediction models as the input vectors of one combined prediction model, and the observed flow data as the output vector, one combined prediction model with four inputs and one output was constructed. Then, using the result of the one combined prediction model as the input vector for two combined prediction models, and the observed flow data as the output vector, two combined prediction models with four inputs and one output were constructed. Using the same method, multiple combined prediction models with 12 kinds of construction schemes were constructed. Taking the Yili River Yamadu hydrological station in Xinjiang as an example, its annual runoff prediction results were compared with those of four kinds of single BP models and IEA-BP models. The results show that the prediction accuracy and generalization ability of multiple combined prediction models are improved as compared with the single prediction model, and with the increase of the combined number of model, the prediction accuracy tends to be improved. Multiple combined prediction models can improve the prediction accuracy.关键词
径流预测/组合模型/BP神经网络/Elman神经网络/RBF神经网络/GRNN神经网络Key words
runoff prediction/combined model/back-propagation neural network/Elman neural network/radial basis function neural network/generalized regression neural network分类
建筑与水利
