长江科学院院报 ›› 2020, Vol. 37 ›› Issue (7): 47-52.DOI: 10.11988/ckyyb.20190326

• 水资源与环境 • 上一篇    下一篇

基于VMD-BP模型的河流流量预测方法

赵力学1, 黄解军1, 程学军2, 申邵洪2, 袁艳斌1   

  1. 1.武汉理工大学 资源与环境工程学院,武汉 430070;
    2.长江科学院 空间信息技术应用研究所,武汉 430010
  • 收稿日期:2019-03-27 出版日期:2020-07-01 发布日期:2020-08-06
  • 通讯作者: 黄解军(1976-),男,湖南永州人,教授,博士,从事水文信息化研究工作。E-mail: hjj@whut.edu.cn
  • 作者简介:赵力学(1994-),男,山西晋中人,硕士研究生,从事水文信息化研究工作。E-mail: zlx_whut@126.com
  • 基金资助:
    国家自然科学基金项目(41571514);长江科学院开放研究基金项目(CKWV2018499/KY)

A Method of River Flow Prediction Based on VMD-BP Model

ZHAO Li-xue1, HUANG Jie-jun1, CHENG Xue-jun2, SHEN Shao-hong2, YUAN Yan-bin1   

  1. 1. School of Resources and Environmental Engineering, Wuhan University of Technology, Wuhan 430070, China;
    2. Department of Spatial Information Technology Application, Yangtze River Scientific Research Institute, Wuhan 430010, China
  • Received:2019-03-27 Online:2020-07-01 Published:2020-08-06

摘要: 河流流量是水文监测和水资源管理的重要指标,流量预测对于水利建设、航运规划和水资源调度等方面具有重要的指导意义和参考价值。结合变分模态分解(VMD)处理非平稳序列的优势以及BP神经网络(BPNN)处理非线性拟合的能力,提出和构建了基于VMD-BP模型的河流流量预测方法。以长江宜昌水文站为实例,基于1998年和1999年的日水位和日流量数据,对方法模型进行了验证。结果表明:VMD-BP模型在一定程度上解决了水位和流量的多值关系,降低了数据的波动性,预测结果优于线性拟合的回归模型和BPNN模型,预测误差仅为1.61%,为河流流量预测提供了一种有效的方法。

关键词: 河流流量, 变分模态分解, BP神经网络, 预测模型, 水位-流量关系, 宜昌水文站

Abstract: River flow is an important ecological indicator for monitoring hydrological issues and managing water resources. Flow forecast is also significant for providing guidance and reference for water conservancy construction, navigation planning, and water resources dispatching. In the present research, a VMD-BP (variational mode decomposition and back propagation) model of forecasting river flow is proposed and constructed by combining the advantages of VMD in dealing with non-stationary sequences and the ability of BP neural network in tackling nonlinear fitting problems. The model is verified by using daily water level and flow data in 1998 and 1999 at Yichang Hydrological Station of the Yangtze River. Results indicate the VMD-BP model solves the multi-value relations between water level and flow to some extent and mitigates the volatility of data. The predicted result is better than those of linear fitting regression model and BPNN model, and the prediction error is merely 1.61%. Therefore, the VMD-BP model can be considered as an effective method for river flow prediction.

Key words: river flow, variational mode decomposition, BP neural network, prediction model, stage-discharge relation, Yichang hydrologic station

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