基于VMD-BP模型的河流流量预测方法

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。

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

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.