BP神经网络组合模型在次洪量预测中的应用

目的]探讨BP神经网络组合模型在次洪量预测中的应用,为黄土高原淤地坝群的安全度汛提供决策依据。方法]构建基于多元线性回归模型(MLR)和去趋势互相关分析法(DCCA)的BP神经网络组合模型;选择均方差(MSE)、平均绝对误差(MAE)、平均绝对百分比误差(MAPE)以及确定性系数(DC)作为评价指标,与单一模型(多元线性回归模型、BP神经网络模型以及去趋势互相关分析法)进行比较。结果]BP神经网络组合模型的4项指标MSE,MAE,MAPE和DC分别为2.144,5.453,0.074和0.988,均优于单一模型;模型预测效果从优到劣分别为BP神经网络组合模型、BP神经网络模型、多元线性回归模型和去趋势互相关分析法。结论]BP神经网络组合模型较单一模型平稳性增强,提高了预测效果,可用于淤地坝群的次暴雨洪量预测。

Application of Optimized BP Neural Network Combined Model in Forecasting Flood Discharge

Objective] To provide a reference for the flood-control safety of the loess plateau check dam system, a BP neural network combination model was tried to apply for predicting runoff from a storm-flood event.Methods] The BP neural network(BPNN) combination model(BPNNC) was constructed on the base of multiple linear regression model(MLR) and detrended cross-correlation analysis(DCCA). Its output was compared with those from other three single models(MLR, BP neural network and DCCA) by the model evaluation indexes of mean square error(MSE), mean absolute error(MAE), mean absolute percentage error(MAPE), and deterministic coefficient(DC).Results] The four values of MSE, MAE, MAPE and DC from BP neural network combination model were 2.144, 5.453, 0.074 and 0.988, respectively, which were better than the ones of the single models. The order of model precisions from high to low was BP neural network combination model, BP neural network model, multiple linear regression model and detrended cross-correlation analysis, successively.Conclusion] The BP neural network combination model is more stable as compared with the single models, which can be used to predict the runoff from a storm-flood event.