改进BP神经网络集成模型在径流预测中的应用

为提高径流预测预报的精度和泛化能力,建立了基于3种基本改进算法的BP神经网络集成预测模型。利用ADF单位根检验方法、自相关分析方法确定径流时间序列的平稳性和模型的输入向量。针对BP神经网络标准算法收敛速度慢、易陷入局部极值的缺陷,采用自适应动量梯度法、共轭梯度法和Levenberg-Marquardt法分别改进BP神经网络标准算法,依次构建基于3种改进算法的BP神经网络模型对文山州南利河董湖水文站年径流进行预测,并构建GA-BP预测模型作为对比模型;采用加权平均方法对各单一模型预测结果进行综合集成。结果表明:集成模型对南利河2001-2005年径流预测平均相对误差绝对值为4.67%,最大相对误差绝对值为7.11%,精度和泛化能力均优于各单一模型和GA-BP模型。集成模型克服了单一模型预测精度不高和误差不稳定的缺点,具有较好的预测精度和泛化能力,是提高径流预测预报精度的有效方法。

Application of integrated model of improved BP neural network in prediction of runoff

In order to improve the precision and generalization ability of runoff forecast, the paper put forward integrated forecast model of BP neural network based on 3 kinds of basic improved algorithm. The autocorrelation analysis method and ADF unit root test method are used to determine input vector and smooth model of runoff time series. According to the standard of BP neural network algorithm with slow convergence, easy to fall into local minimum defect respectively, the paper improved the standard of BP neural network algorithm by using adaptive momentum gradient method, conjugate gradient method and Levenberg-Marquardt method, constructed 3 kinds of improved BP neural network model to predict the annual runoff at Nagara River Wenshan Donghu lake hydrological station,and contructed GA-BP model as comparison model. It integrated the results of the single forecastmodel by using weighted average method. The results show that the average absolute value of relative error of annual runoff of the Nagara River from 2001 to 2005 forecasted by integrated model is 4.67%, the maximum absolute value of relative error is 7.11%, the accuracy and generalization ability are better than that of the single model and GA-BP model. The integration overcame the disadvantages of single model prediction accuracy is not high and the error is not stable, has good prediction accuracy and generalization ability and is an effective method to improve prediction of runoff forecast.