BP神经网络组合模型在次洪量预测中的应用
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

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);陕西省水利厅项目(2016slkj-12);国家重点研发计划项目(2016YFC0402704)


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

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    [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。

    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 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.

    参考文献
    相似文献
    引证文献
引用本文

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

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2017-03-09
  • 最后修改日期:2017-05-16
  • 录用日期:
  • 在线发布日期: 2018-01-19
  • 出版日期: