水土保持通报2017，Vol.37Issue(6)：173-177,5.DOI:10.13961/j.cnki.stbctb.2017.06.029
BP神经网络组合模型在次洪量预测中的应用
Application of Optimized BP Neural Network Combined Model in Forecasting Flood Discharge
摘要
Abstract
[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 crosscorrelation 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.关键词
淤地坝/次洪量预测/BP神经网络组合模型Key words
check dam/runoff prediction for a storm-flood event/BP neural network combination model分类
天文与地球科学引用本文复制引用
冯鑫伟,黄领梅,沈冰..BP神经网络组合模型在次洪量预测中的应用[J].水土保持通报,2017,37(6):173-177,5.基金项目
国家自然科学基金项目“基于溯源重构的淤地坝影响下设计洪峰计算理论”(51679184) （51679184）
陕西省水利厅项目(2016slkj-12) （2016slkj-12）
国家重点研发计划项目(2016YFC0402704) （2016YFC0402704）
